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# Notation
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A dataset contains *p* features and has *n* datapoints
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so s dataset that has weight, length, age of 1000 people has
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p=3 features and n=1000 points.
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# Chapter 2 : Statistical learning
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## 2.1 What is statistical learning?
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Statistical learning is the method were we take in a set of **features, predictors, or independent variables**
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to predict the **response or dependent variable**
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The first are generally called X and the latter called y.
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The function looks like this:
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y = f(X) + $\epsilon$
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where f(X) is the systematic info about y and $\epsilon$ is the random, non-reducable error. y(X) also contains an error,
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but that can be reduced by selecting another, more appropriate model to train. $\epsilon$ is nonzero, because it may contain variables we haven't measured, for example if we were predicting how a patient might respond to drugs but didn't measure the weight of patients, or may contain unmeasurable variances, such as manufacturer variance in pillmaking so each pill doesn't contain exactly the same ammount of active ingredient.
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The irreducable error will always place an upper bound on the accuracy of our model, and in practice, we will not know what the value of this error is.
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### Inference
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We don't always want to predict y, we sometimes just want to know the relationship betwen X and y. When doing this, we don't want $\hat{y}$ to be treated like a black box.
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In this setting, we may be seeking one or more of the following answers:
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1. Which predictors are associated with the response? It's often the case that only some of the features are ehavily associated with y, and identifying the few important factors out of a large set of variables may be worthwile.
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2. What is the relationship between the response and each predictor? Features may have a possitive or negative or no correlation with theresponse. Depending on the complexity of f, the relationship between the response and a given predictor may also depend on the values of the other predictors.
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3. Can the relationship between Y and each predictor be adequately summarized using a linear equation, or is the relationship more complicated? Historically, we've used linear methods to estimate f(X), which sometimes is reasonable and desirable, but often the relationship is more complicated.
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Modeling can also be done for both inference and prediction purposes. Depending on what our goal is, different methods of estimating f will be appropriate. For example, *linear models* allow for relatively simple and interpretable inference, but may not yield as accurate predictions, while some highly nonlinear approaches may provide very accurate predictions, but will be less interpretable.
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### 2.1.2 How do we estimate f?
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We'll explore various approaches throughout this book, and these book generally share some characteristics. We will always assume that we observed a set of *n* datapiubts, and these are called training data because we'll use this data to train, teach, fit the model. $x_{ij}$ is the value of the j'th predictor for observation i.
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Correspondingly, $y_i$ is the response variable for the i'th observation.
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Then our training data consists of
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$\{(x_1, y_1), (x_2, y_2), \dotso (x_n, y_n)\}$ where $x_i = (x_{i1}, x_{i2}, \dotso , x_{ip} )^T$
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Our goal is to apply a method to the training data in order to estimate the unknown function f. In other words, we want to find the function $\hat{y}$ such that $Y \approx \hat{f}(X)$ for any observation (X,Y). Broadly speaking, most methods can be divided into either *parametric* or *non-parametric.*
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### Parametric methods
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These methods invove a 2 step model-based approach
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1. First, we make an assumption about the form or shape of f. For example, a simple assumption is that f is linear:
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f(X) = a0 + b1 X1 + b2 X2 + ... + bp Xp
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This is a linear model, extensively discussed in chapter 3. Once this assumption is made, the problem of estimating is greatly simplified. Instead of having to estimate an entirely arbitrary p-dimensional function f(X), we only need to estimate p+1 coefficients.
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2. After a model has been selected, we use a procedure that fits or trains the model. In case of the linear model, we need to estimate the parameters. The most common approach to fitting the model is the *(ordinary) least squares.* However, this is only one of the possible ways.
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The model based approach is refered to as parametric; it reduces the problem of estimating f down to estimating a set of parameters. The downside is that the chosen model will usually not match the true unknown form of f. If our model is too far from true f, our estimate is poor. We can try to adress this problem by choosing a *flexible* model that can fit many different possile functional forms of f. But in general, fitting a more flexible model requires estimating a greater number of parameters, and these more complex models can lead to *overfitting* the data, meaning they follow the errors/noise too closely.
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### Nonparametric methods
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These methods don't make explicit assumptions about the functional form of f. Instead, they seek an estimate of f that gets as close as possible to the datapoints without being too rough or wiggly.
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These approaches have the potential to acurately fit a wider range of possible shapes of f. The major disadvantage is that since they don't reduce the problem to a small number of parameters, they need far more observations to train on.
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This method tries to produce an estimate for f that is as close as possible to the data that's as smooth as possible. (Look into thin plane spline) In order to fit a thin-plate spline, we must select a level of smoothness. In general, the lower the level of smoothness (the rougher the tps), the higher the chance of overfitting. We'll discuss choosing the correct ammount of smoothness in later chapters.
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## 2.1.3 Tradeoff between prediction accuracy and model interpretability
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Of the multitude of methods we'll examine, some are less flexible or more restrictive, in the sense that they can produce a relatively small range of shapes to estimate f. For example, linear regression is a rel. inflexible approach, because it only generates linear functions.
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Why would we ever choose a more restrictive method, then?
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If we're interested in inference, we want a more interpretable model. very flexible approaches, can lead to such complicated estimates of f that it is difficult to understand how any individual predictor is associated with the response.
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This doesn't mean that when we only want to predict,that we should choose the most flexible model. This will not always yield the most accurate prediction, because the potential of overfitting is larger.
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### 2.1.4 Supervised vs unsupervised learning
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We can also divide the statistical learning problems like so. Thus far, we've only looked at examples in the supervised learning domain. Many classical statisitcal learning methods such as linear regression, logistic regression, GAMs, boosting and support vector machines fall into the supervised category, and the majority of this book falls into this category.
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Supervised mean that for every $x_i$, we have a response $y_i$. In unsupervised, we have xi but no associated yi. We lack a response variable that can supervise our analysis.
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What statistical analysis is possible, then? We can seek to undersand the relationship between variables or between observations. One statistical learning tool we can use is cluster analysis or clustering, where the goal is to say wether observations fall into relatively distinct groups. For example, a marketing segmentation study where we observe multiple variables for potential customers, such as income, location and shopping habbits. We might believe we have groups such as big spenders and low spenders. We could try to cluser the customers based on the available vars into these groups.
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This might be easy with a visual inspection if we have 2 vars, but is practically impossible when he have more than 3 vars, which we often do.
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Sometimes, the question of wether we're using a supervised or unsupervised method is less clearcut. We may have n observations and m < n observations. This arises when the response is more difficult/expensive to collect compared to the inputs. We refer to these kind of problems as semi-supervised learning problems, and these are beyond the scope of this book.
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### 2.1.5 Regression vs classification problems
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Vars can be divided in quantitative or qualitative(categorical)
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We tend to refer to problems with a quantitative response as regression problems, while those involving qualitative responses as classification problems. The distinction isn't always distinct. logistic regression is often used with qualitative binary response. But since it estimates class probabilitites, it can be thought of as a regression method as well.
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We tend to select the method on the basis of wether the problem is quantitative or qualitative, we might use linear regression when quantitative and logistic regression when qualitative. However, wether the features are qualitative or quantitative is less important. Most methods discussed in this book can be used regardless of predictor type, provided that we properly *code* the qualitative predictors before the analysis.
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## 2.2 Assessing model accuracy
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Different approaches produce different results, and some approaches will be more appropriate and accurate for a given problem.
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We will discuss some of the most important concepts that arise in selecting a method for a specific data set. We'll explain how these can be applied later on.
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### 2.2.1 Measuring the quality of fit
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In order to measure the performance of a learnign method on a data set, we need a way to measure how well the predictions match the observed data. When using regression, the most used measure is *mean squared error* MSE. It's the summation of the squared difference between predicted response and actual response, divided by the number of rpedictions.
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We typically split all the available data in a training set, and a set to get the accuracy from.
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A fundamental property of stat. learning methods is that as model flexibility increses, training MSE decreases, but test MSE may not. When a given method yields a small training MSE but large test MSE, we are overfitting the data. This happens when the method is working too hard to find patterns in the training data and may pick up patterns caused by random chance. when we overfit, the test MSE will be very large because the supposed pattern that the method found simply doesn't exist in the test data. We should note that test MSE will always be larger than training MSE. **Overfitting specifically refers to cases where a less flexible bodel would have yielded a smaller test MSE**
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In practice, its usually easy to compute training MSE, but test MSE may be harder because there's no test data available.
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As we can see in the examples, the flexibility level corresponding with minimal test MSE can vary considerbly among data sets. We;ll discuss a number of appoaches that can be used to estimate the minimum point. An important method is cross validation.
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## 2.2.2 The bias variance tradeoff
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It can be shown that the expected test MSE can be decomposed in the sum of the variance and squared bias of expected f and the variance of error term e
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$$
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E(y_0- \hat{f}(x_0))^2 = Var(\hat{f}) + (bias(\hat{{f}}))^2 + Var(\epsilon)
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$$
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This equation tells us that in order to minimize the expected error, we need a method that both archieves a low variance as a low bias.
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**Variance** refers to the amount by which f would change if we
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estimated it using a different training data set. In general, more flexible statistical methods have higher variance.
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On the other hand, **bias** refers to the error that is introduced by approximating a reallife problem, which may be extremely complicated, by a much simpler model. Generally, more flexible methods result in less bias. As a general rule, as we use more flexible methods, the variance will increase and the bias will decrease.
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`The relative rate of change of these two quantities determines whether the test MSE increases or decreases. As we increase the flexibility of a class of methods, the bias tends to initially decrease faster than the variance increases Consequently, the expected test MSE declines. However, at some point increasing flexibility has little impact on the bias but starts to significantly increase the variance. When this happens the test MSE increases.
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The challenge lies in finding a method for which both the variance and the squared bias are low. This trade-off is one of the most important recurring themes in this book.
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## 2.2.3 The classification setting
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So far, our discussion of model accuracy has been focused on regression, but many of the concepts also transfer over to the classification setting wih only some minimal modification due to the fact that y is no longer numerical.
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We now use error rate, the average of times where expected y is not y.
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### the Bayes classifier
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It is possible to show that the test error rate given in (2.9) is minimized, on average, by a very simple classifier that assigns each observation to the most likely class, given its predictor values. In other words, we should simply assign a test observation with predictor vector x0 to the class j for which the probability conditional that Y = j given the observed predictor vector x0 is the largest.
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$$
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P(Y=j| X=x_0)
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$$
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This very simple classifier is called the Bayes classifier.
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In a 2class/binary problem, the Bayes classifier corresponds to class1 if the probability P(Y=1| X=x0) > 0.5, and to the other if otherwise.
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The Bayes classifier produces the lowest possible test error rate, called the *Bayes error rate*. Since the Bayes classifier will always choose the class for which the probability is the largest, the error rate at X = x0 will be 1−maxj P(Y=j| X=x0).
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In general, the Bayes error rate is given by
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$$
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1 - E( max j P(Y=j| X=x0) )
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$$
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where the expectation averages the probability over all possible values of X.
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## K nearest neighbours
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In theory we would always like to predict qualitative responses using the Bayes classifier. But for real data, we do not know the conditional distribution of Y given X, and so computing the Bayes classifier is impossible. Many approaches attempt to estimate the conditional distribution of Y given X, and then classify a given observation to the class with highest estimated probability. One such method is the K-nearest neighbors (KNN) classifier.
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Given a number K and x0, it first identifies the K training points closest to x0 (repr. by $\mathcal{N}_0$.) It then estimates the conditional probability for class j as the fraction of points in N0 whose response values equal j. Finally, KNN applies Bayes rule and classifies the test observation x0 to the class with the largest probability.
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So, for example, if i have xi and k=9, and 7 out of the 9 neighbours are B and 2 are A, we'll get a 7/9th probability that xi is B.
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The choice of K has a drastic effect on the KNN classifier obtained. When K is low, the decision boundary is overly flexible and finds patterns in the data that don’t correspond to the Bayes decision boundary. This corresponds to a classifier that has low bias but
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very high variance. As K grows, the method becomes less flexible and
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produces a decision boundary that is close to linear. This corresponds to a low-variance but high-bias classifier. Just as in regression, there is a similar disparity between training error rate and test error rate.
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Excercises:
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For each of parts (a) through (d), indicate whether we would generally
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expect the performance of a flexible statistical learning method to be
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better or worse than an inflexible method. Justify your answer.
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## I. The sample size n is extremely large, and the number of predictors p is small.
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**I expect the variance(the variance i learned at school) between datasets to be low since it's a huge dataset, since there are many data points, so what we could try to do is select a method that decreases the bias. Since bias tends to decrease faster than variance increases as flexibility increases, I'd try to go with a more flexible method**
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2. The number of predictors p is extremely large, and the number
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of observations n is small.
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**Small number of observations already points towards using a less flexible method, since these generally need less datapoints. So I'd go towards that direction. And since the previous question is essentially the opposite, and i selected the more flexible method, i guess this would take the less flexible method**
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3. The relationship between the predictors and response is highly
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non-linear.
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**Now, we can say that if we chose an inflexible one, we'd get more bias, because we're trying to greatly simplify the model. I would go towards a more flexible method. At least, not a method that can only handle y being linear**
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4. The variance of the error terms, i.e. σ 2 = Var(), is extremely
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high.
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**I would try a method that tried to reduce the variance, so this would point me towards a method that's less flexible**
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## Explain whether each scenario is a classification or regression problem, and indicate whether we are most interested in inference or prediction. Finally, provide n and p.
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We collect a set of data on the top 500 firms in the US. For each firm we record profit, number of employees, industry and the CEO salary. We are interested in understanding which factors affect CEO salary.
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**n=500 and p = 4. This looks more like an inference problem than a prediction problem, since you're interested in the relationship between predictors and response. I'd also say that it looks more like a classification problem, in the sense that we'd be interested if the salary goes up/down as x1, x2, etc increases**
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We are considering launching a new product and wish to know
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whether it will be a success or a failure. We collect data on 20
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similar products that were previously launched. For each prod-
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uct we have recorded whether it was a success or failure, price
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charged for the product, marketing budget, competition price,
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and ten other variables.
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**This is definitely a classification problem, and we're more interested in prediciton than inference. We have n=20 and p = 14**
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We are interested in predicting the % change in the USD/Euro
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exchange rate in relation to the weekly changes in the world
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stock markets. Hence we collect weekly data for all of 2012. For
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each week we record the % change in the USD/Euro, the %
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change in the US market, the % change in the British market,
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and the % change in the German market.
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**Prediction over inference, and regression. p=4 and n=52 * 8**
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Explain why each of the five curves has the shape displayed in
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part (a).
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1. Bias. This is the error that occurs by the discripancy between the model and the real world problem. As flexibility increases, this allows us to create a model that's more similar to the real world problem, and thus bias decreases.
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2. Variance. as flexibility increases, we're fitting the data more closely, and may get patterns caused by random chance. This means that when we use the model on another data set, which probably won't have those patterns, we will get a higher variance.
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3. this is the addition of the above 2.
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4. this is loosely linked to 3.
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5. This is the irreducable error, neither increasing or decreasing the error should have an effect.
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Describe three real-life applications in which classification might
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be useful. Describe the response, as well as the predictors. Is the
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goal of each application inference or prediction? Explain your
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answer.
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**We could try predict wether the stock of a company goes up or down. It's primarily prediction, and the responses would be up/down. The predictors would be competitors and past stock history.
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We could try to see wether some disseases increase the likelyhood of havind diabetes. This would be an inference problem, with predictors being having various other disseases, and response being yes/no. We could try to look at age, sickness, activity level, stuff like that and mortality rate of corona virus patients. This would be an inference problem, and the response whould be yes/no death.**
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Do the same, but with regression.
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**Stock price, with predictors being competitors, downstream suppliers, past stock prices. Test score, with predictors like past test scores, socioeconomic standing, hours spent studying, classes missed. Value of some art thing, predictors being artist, genre, date, twitter followers of artist**
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Describe three real-life applications in which cluster analysis
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might be useful.
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**party alliance, with predictors being location, income, age, gender. Loan riskiness, with income, occupation, age. The last problem mentioned in classifiing problems**
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What are the advantages and disadvantages of a very flexible (versus
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a less flexible) approach for regression or classification? Under what
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circumstances might a more flexible approach be preferred to a less
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flexible approach? When might a less flexible approach be preferred
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**A more flexible approach decreases the bias and allows us to model the problem closer to the real world. We prefer a more flexible approach if we know the problem isn't linear, if we have a lot of datapoints, if we're looking for prediction rather than inference. We might want the less flexible methods if we dont have a lot of data, if we want inference, if we know the problem is linear, if we have less computing power.**
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Describe the differences between a parametric and a non-parametric
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statistical learning approach. What are the advantages of a para-
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metric approach to regression or classification (as opposed to a non-
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parametric approach)? What are its disadvantages?
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**Parametric needs less datapoints to be trained, needs less computing power, is less flexible, is linear. Advantages include needing less datapoints and being more interpretable. Disadvantages include having more bias because of the linearity.**
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k=1: green.
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K=3 : 2/3 red
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I have no idea
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ISLR/pics/ch2-3.png
Normal file
BIN
ISLR/pics/ch2-3.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 74 KiB |
BIN
ISLR/pics/ch3-1.png
Normal file
BIN
ISLR/pics/ch3-1.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 12 KiB |
BIN
ISLR/pics/ch3-2.png
Normal file
BIN
ISLR/pics/ch3-2.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 21 KiB |
BIN
ISLR/pics/ch3-3.png
Normal file
BIN
ISLR/pics/ch3-3.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 11 KiB |
BIN
ISLR/pics/ch3-4.png
Normal file
BIN
ISLR/pics/ch3-4.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 25 KiB |
BIN
ISLR/pics/ch3-5.png
Normal file
BIN
ISLR/pics/ch3-5.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 10 KiB |
BIN
ISLR/pics/ch3-6.png
Normal file
BIN
ISLR/pics/ch3-6.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 37 KiB |
BIN
ISLR/pics/ch3-7.png
Normal file
BIN
ISLR/pics/ch3-7.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 9.9 KiB |
BIN
ISLR/pics/ch3-8.png
Normal file
BIN
ISLR/pics/ch3-8.png
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|
After Width: | Height: | Size: 116 KiB |
201
datasets/Advertising.csv
Normal file
201
datasets/Advertising.csv
Normal file
@ -0,0 +1,201 @@
|
|||||||
|
,TV,radio,newspaper,sales
|
||||||
|
1,230.1,37.8,69.2,22.1
|
||||||
|
2,44.5,39.3,45.1,10.4
|
||||||
|
3,17.2,45.9,69.3,9.3
|
||||||
|
4,151.5,41.3,58.5,18.5
|
||||||
|
5,180.8,10.8,58.4,12.9
|
||||||
|
6,8.7,48.9,75,7.2
|
||||||
|
7,57.5,32.8,23.5,11.8
|
||||||
|
8,120.2,19.6,11.6,13.2
|
||||||
|
9,8.6,2.1,1,4.8
|
||||||
|
10,199.8,2.6,21.2,10.6
|
||||||
|
11,66.1,5.8,24.2,8.6
|
||||||
|
12,214.7,24,4,17.4
|
||||||
|
13,23.8,35.1,65.9,9.2
|
||||||
|
14,97.5,7.6,7.2,9.7
|
||||||
|
15,204.1,32.9,46,19
|
||||||
|
16,195.4,47.7,52.9,22.4
|
||||||
|
17,67.8,36.6,114,12.5
|
||||||
|
18,281.4,39.6,55.8,24.4
|
||||||
|
19,69.2,20.5,18.3,11.3
|
||||||
|
20,147.3,23.9,19.1,14.6
|
||||||
|
21,218.4,27.7,53.4,18
|
||||||
|
22,237.4,5.1,23.5,12.5
|
||||||
|
23,13.2,15.9,49.6,5.6
|
||||||
|
24,228.3,16.9,26.2,15.5
|
||||||
|
25,62.3,12.6,18.3,9.7
|
||||||
|
26,262.9,3.5,19.5,12
|
||||||
|
27,142.9,29.3,12.6,15
|
||||||
|
28,240.1,16.7,22.9,15.9
|
||||||
|
29,248.8,27.1,22.9,18.9
|
||||||
|
30,70.6,16,40.8,10.5
|
||||||
|
31,292.9,28.3,43.2,21.4
|
||||||
|
32,112.9,17.4,38.6,11.9
|
||||||
|
33,97.2,1.5,30,9.6
|
||||||
|
34,265.6,20,0.3,17.4
|
||||||
|
35,95.7,1.4,7.4,9.5
|
||||||
|
36,290.7,4.1,8.5,12.8
|
||||||
|
37,266.9,43.8,5,25.4
|
||||||
|
38,74.7,49.4,45.7,14.7
|
||||||
|
39,43.1,26.7,35.1,10.1
|
||||||
|
40,228,37.7,32,21.5
|
||||||
|
41,202.5,22.3,31.6,16.6
|
||||||
|
42,177,33.4,38.7,17.1
|
||||||
|
43,293.6,27.7,1.8,20.7
|
||||||
|
44,206.9,8.4,26.4,12.9
|
||||||
|
45,25.1,25.7,43.3,8.5
|
||||||
|
46,175.1,22.5,31.5,14.9
|
||||||
|
47,89.7,9.9,35.7,10.6
|
||||||
|
48,239.9,41.5,18.5,23.2
|
||||||
|
49,227.2,15.8,49.9,14.8
|
||||||
|
50,66.9,11.7,36.8,9.7
|
||||||
|
51,199.8,3.1,34.6,11.4
|
||||||
|
52,100.4,9.6,3.6,10.7
|
||||||
|
53,216.4,41.7,39.6,22.6
|
||||||
|
54,182.6,46.2,58.7,21.2
|
||||||
|
55,262.7,28.8,15.9,20.2
|
||||||
|
56,198.9,49.4,60,23.7
|
||||||
|
57,7.3,28.1,41.4,5.5
|
||||||
|
58,136.2,19.2,16.6,13.2
|
||||||
|
59,210.8,49.6,37.7,23.8
|
||||||
|
60,210.7,29.5,9.3,18.4
|
||||||
|
61,53.5,2,21.4,8.1
|
||||||
|
62,261.3,42.7,54.7,24.2
|
||||||
|
63,239.3,15.5,27.3,15.7
|
||||||
|
64,102.7,29.6,8.4,14
|
||||||
|
65,131.1,42.8,28.9,18
|
||||||
|
66,69,9.3,0.9,9.3
|
||||||
|
67,31.5,24.6,2.2,9.5
|
||||||
|
68,139.3,14.5,10.2,13.4
|
||||||
|
69,237.4,27.5,11,18.9
|
||||||
|
70,216.8,43.9,27.2,22.3
|
||||||
|
71,199.1,30.6,38.7,18.3
|
||||||
|
72,109.8,14.3,31.7,12.4
|
||||||
|
73,26.8,33,19.3,8.8
|
||||||
|
74,129.4,5.7,31.3,11
|
||||||
|
75,213.4,24.6,13.1,17
|
||||||
|
76,16.9,43.7,89.4,8.7
|
||||||
|
77,27.5,1.6,20.7,6.9
|
||||||
|
78,120.5,28.5,14.2,14.2
|
||||||
|
79,5.4,29.9,9.4,5.3
|
||||||
|
80,116,7.7,23.1,11
|
||||||
|
81,76.4,26.7,22.3,11.8
|
||||||
|
82,239.8,4.1,36.9,12.3
|
||||||
|
83,75.3,20.3,32.5,11.3
|
||||||
|
84,68.4,44.5,35.6,13.6
|
||||||
|
85,213.5,43,33.8,21.7
|
||||||
|
86,193.2,18.4,65.7,15.2
|
||||||
|
87,76.3,27.5,16,12
|
||||||
|
88,110.7,40.6,63.2,16
|
||||||
|
89,88.3,25.5,73.4,12.9
|
||||||
|
90,109.8,47.8,51.4,16.7
|
||||||
|
91,134.3,4.9,9.3,11.2
|
||||||
|
92,28.6,1.5,33,7.3
|
||||||
|
93,217.7,33.5,59,19.4
|
||||||
|
94,250.9,36.5,72.3,22.2
|
||||||
|
95,107.4,14,10.9,11.5
|
||||||
|
96,163.3,31.6,52.9,16.9
|
||||||
|
97,197.6,3.5,5.9,11.7
|
||||||
|
98,184.9,21,22,15.5
|
||||||
|
99,289.7,42.3,51.2,25.4
|
||||||
|
100,135.2,41.7,45.9,17.2
|
||||||
|
101,222.4,4.3,49.8,11.7
|
||||||
|
102,296.4,36.3,100.9,23.8
|
||||||
|
103,280.2,10.1,21.4,14.8
|
||||||
|
104,187.9,17.2,17.9,14.7
|
||||||
|
105,238.2,34.3,5.3,20.7
|
||||||
|
106,137.9,46.4,59,19.2
|
||||||
|
107,25,11,29.7,7.2
|
||||||
|
108,90.4,0.3,23.2,8.7
|
||||||
|
109,13.1,0.4,25.6,5.3
|
||||||
|
110,255.4,26.9,5.5,19.8
|
||||||
|
111,225.8,8.2,56.5,13.4
|
||||||
|
112,241.7,38,23.2,21.8
|
||||||
|
113,175.7,15.4,2.4,14.1
|
||||||
|
114,209.6,20.6,10.7,15.9
|
||||||
|
115,78.2,46.8,34.5,14.6
|
||||||
|
116,75.1,35,52.7,12.6
|
||||||
|
117,139.2,14.3,25.6,12.2
|
||||||
|
118,76.4,0.8,14.8,9.4
|
||||||
|
119,125.7,36.9,79.2,15.9
|
||||||
|
120,19.4,16,22.3,6.6
|
||||||
|
121,141.3,26.8,46.2,15.5
|
||||||
|
122,18.8,21.7,50.4,7
|
||||||
|
123,224,2.4,15.6,11.6
|
||||||
|
124,123.1,34.6,12.4,15.2
|
||||||
|
125,229.5,32.3,74.2,19.7
|
||||||
|
126,87.2,11.8,25.9,10.6
|
||||||
|
127,7.8,38.9,50.6,6.6
|
||||||
|
128,80.2,0,9.2,8.8
|
||||||
|
129,220.3,49,3.2,24.7
|
||||||
|
130,59.6,12,43.1,9.7
|
||||||
|
131,0.7,39.6,8.7,1.6
|
||||||
|
132,265.2,2.9,43,12.7
|
||||||
|
133,8.4,27.2,2.1,5.7
|
||||||
|
134,219.8,33.5,45.1,19.6
|
||||||
|
135,36.9,38.6,65.6,10.8
|
||||||
|
136,48.3,47,8.5,11.6
|
||||||
|
137,25.6,39,9.3,9.5
|
||||||
|
138,273.7,28.9,59.7,20.8
|
||||||
|
139,43,25.9,20.5,9.6
|
||||||
|
140,184.9,43.9,1.7,20.7
|
||||||
|
141,73.4,17,12.9,10.9
|
||||||
|
142,193.7,35.4,75.6,19.2
|
||||||
|
143,220.5,33.2,37.9,20.1
|
||||||
|
144,104.6,5.7,34.4,10.4
|
||||||
|
145,96.2,14.8,38.9,11.4
|
||||||
|
146,140.3,1.9,9,10.3
|
||||||
|
147,240.1,7.3,8.7,13.2
|
||||||
|
148,243.2,49,44.3,25.4
|
||||||
|
149,38,40.3,11.9,10.9
|
||||||
|
150,44.7,25.8,20.6,10.1
|
||||||
|
151,280.7,13.9,37,16.1
|
||||||
|
152,121,8.4,48.7,11.6
|
||||||
|
153,197.6,23.3,14.2,16.6
|
||||||
|
154,171.3,39.7,37.7,19
|
||||||
|
155,187.8,21.1,9.5,15.6
|
||||||
|
156,4.1,11.6,5.7,3.2
|
||||||
|
157,93.9,43.5,50.5,15.3
|
||||||
|
158,149.8,1.3,24.3,10.1
|
||||||
|
159,11.7,36.9,45.2,7.3
|
||||||
|
160,131.7,18.4,34.6,12.9
|
||||||
|
161,172.5,18.1,30.7,14.4
|
||||||
|
162,85.7,35.8,49.3,13.3
|
||||||
|
163,188.4,18.1,25.6,14.9
|
||||||
|
164,163.5,36.8,7.4,18
|
||||||
|
165,117.2,14.7,5.4,11.9
|
||||||
|
166,234.5,3.4,84.8,11.9
|
||||||
|
167,17.9,37.6,21.6,8
|
||||||
|
168,206.8,5.2,19.4,12.2
|
||||||
|
169,215.4,23.6,57.6,17.1
|
||||||
|
170,284.3,10.6,6.4,15
|
||||||
|
171,50,11.6,18.4,8.4
|
||||||
|
172,164.5,20.9,47.4,14.5
|
||||||
|
173,19.6,20.1,17,7.6
|
||||||
|
174,168.4,7.1,12.8,11.7
|
||||||
|
175,222.4,3.4,13.1,11.5
|
||||||
|
176,276.9,48.9,41.8,27
|
||||||
|
177,248.4,30.2,20.3,20.2
|
||||||
|
178,170.2,7.8,35.2,11.7
|
||||||
|
179,276.7,2.3,23.7,11.8
|
||||||
|
180,165.6,10,17.6,12.6
|
||||||
|
181,156.6,2.6,8.3,10.5
|
||||||
|
182,218.5,5.4,27.4,12.2
|
||||||
|
183,56.2,5.7,29.7,8.7
|
||||||
|
184,287.6,43,71.8,26.2
|
||||||
|
185,253.8,21.3,30,17.6
|
||||||
|
186,205,45.1,19.6,22.6
|
||||||
|
187,139.5,2.1,26.6,10.3
|
||||||
|
188,191.1,28.7,18.2,17.3
|
||||||
|
189,286,13.9,3.7,15.9
|
||||||
|
190,18.7,12.1,23.4,6.7
|
||||||
|
191,39.5,41.1,5.8,10.8
|
||||||
|
192,75.5,10.8,6,9.9
|
||||||
|
193,17.2,4.1,31.6,5.9
|
||||||
|
194,166.8,42,3.6,19.6
|
||||||
|
195,149.7,35.6,6,17.3
|
||||||
|
196,38.2,3.7,13.8,7.6
|
||||||
|
197,94.2,4.9,8.1,9.7
|
||||||
|
198,177,9.3,6.4,12.8
|
||||||
|
199,283.6,42,66.2,25.5
|
||||||
|
200,232.1,8.6,8.7,13.4
|
||||||
|
393
datasets/Auto.csv
Normal file
393
datasets/Auto.csv
Normal file
@ -0,0 +1,393 @@
|
|||||||
|
"","mpg","cylinders","displacement","horsepower","weight","acceleration","year","origin","name"
|
||||||
|
"1",18,8,307,130,3504,12,70,1,"chevrolet chevelle malibu"
|
||||||
|
"2",15,8,350,165,3693,11.5,70,1,"buick skylark 320"
|
||||||
|
"3",18,8,318,150,3436,11,70,1,"plymouth satellite"
|
||||||
|
"4",16,8,304,150,3433,12,70,1,"amc rebel sst"
|
||||||
|
"5",17,8,302,140,3449,10.5,70,1,"ford torino"
|
||||||
|
"6",15,8,429,198,4341,10,70,1,"ford galaxie 500"
|
||||||
|
"7",14,8,454,220,4354,9,70,1,"chevrolet impala"
|
||||||
|
"8",14,8,440,215,4312,8.5,70,1,"plymouth fury iii"
|
||||||
|
"9",14,8,455,225,4425,10,70,1,"pontiac catalina"
|
||||||
|
"10",15,8,390,190,3850,8.5,70,1,"amc ambassador dpl"
|
||||||
|
"11",15,8,383,170,3563,10,70,1,"dodge challenger se"
|
||||||
|
"12",14,8,340,160,3609,8,70,1,"plymouth 'cuda 340"
|
||||||
|
"13",15,8,400,150,3761,9.5,70,1,"chevrolet monte carlo"
|
||||||
|
"14",14,8,455,225,3086,10,70,1,"buick estate wagon (sw)"
|
||||||
|
"15",24,4,113,95,2372,15,70,3,"toyota corona mark ii"
|
||||||
|
"16",22,6,198,95,2833,15.5,70,1,"plymouth duster"
|
||||||
|
"17",18,6,199,97,2774,15.5,70,1,"amc hornet"
|
||||||
|
"18",21,6,200,85,2587,16,70,1,"ford maverick"
|
||||||
|
"19",27,4,97,88,2130,14.5,70,3,"datsun pl510"
|
||||||
|
"20",26,4,97,46,1835,20.5,70,2,"volkswagen 1131 deluxe sedan"
|
||||||
|
"21",25,4,110,87,2672,17.5,70,2,"peugeot 504"
|
||||||
|
"22",24,4,107,90,2430,14.5,70,2,"audi 100 ls"
|
||||||
|
"23",25,4,104,95,2375,17.5,70,2,"saab 99e"
|
||||||
|
"24",26,4,121,113,2234,12.5,70,2,"bmw 2002"
|
||||||
|
"25",21,6,199,90,2648,15,70,1,"amc gremlin"
|
||||||
|
"26",10,8,360,215,4615,14,70,1,"ford f250"
|
||||||
|
"27",10,8,307,200,4376,15,70,1,"chevy c20"
|
||||||
|
"28",11,8,318,210,4382,13.5,70,1,"dodge d200"
|
||||||
|
"29",9,8,304,193,4732,18.5,70,1,"hi 1200d"
|
||||||
|
"30",27,4,97,88,2130,14.5,71,3,"datsun pl510"
|
||||||
|
"31",28,4,140,90,2264,15.5,71,1,"chevrolet vega 2300"
|
||||||
|
"32",25,4,113,95,2228,14,71,3,"toyota corona"
|
||||||
|
"34",19,6,232,100,2634,13,71,1,"amc gremlin"
|
||||||
|
"35",16,6,225,105,3439,15.5,71,1,"plymouth satellite custom"
|
||||||
|
"36",17,6,250,100,3329,15.5,71,1,"chevrolet chevelle malibu"
|
||||||
|
"37",19,6,250,88,3302,15.5,71,1,"ford torino 500"
|
||||||
|
"38",18,6,232,100,3288,15.5,71,1,"amc matador"
|
||||||
|
"39",14,8,350,165,4209,12,71,1,"chevrolet impala"
|
||||||
|
"40",14,8,400,175,4464,11.5,71,1,"pontiac catalina brougham"
|
||||||
|
"41",14,8,351,153,4154,13.5,71,1,"ford galaxie 500"
|
||||||
|
"42",14,8,318,150,4096,13,71,1,"plymouth fury iii"
|
||||||
|
"43",12,8,383,180,4955,11.5,71,1,"dodge monaco (sw)"
|
||||||
|
"44",13,8,400,170,4746,12,71,1,"ford country squire (sw)"
|
||||||
|
"45",13,8,400,175,5140,12,71,1,"pontiac safari (sw)"
|
||||||
|
"46",18,6,258,110,2962,13.5,71,1,"amc hornet sportabout (sw)"
|
||||||
|
"47",22,4,140,72,2408,19,71,1,"chevrolet vega (sw)"
|
||||||
|
"48",19,6,250,100,3282,15,71,1,"pontiac firebird"
|
||||||
|
"49",18,6,250,88,3139,14.5,71,1,"ford mustang"
|
||||||
|
"50",23,4,122,86,2220,14,71,1,"mercury capri 2000"
|
||||||
|
"51",28,4,116,90,2123,14,71,2,"opel 1900"
|
||||||
|
"52",30,4,79,70,2074,19.5,71,2,"peugeot 304"
|
||||||
|
"53",30,4,88,76,2065,14.5,71,2,"fiat 124b"
|
||||||
|
"54",31,4,71,65,1773,19,71,3,"toyota corolla 1200"
|
||||||
|
"55",35,4,72,69,1613,18,71,3,"datsun 1200"
|
||||||
|
"56",27,4,97,60,1834,19,71,2,"volkswagen model 111"
|
||||||
|
"57",26,4,91,70,1955,20.5,71,1,"plymouth cricket"
|
||||||
|
"58",24,4,113,95,2278,15.5,72,3,"toyota corona hardtop"
|
||||||
|
"59",25,4,97.5,80,2126,17,72,1,"dodge colt hardtop"
|
||||||
|
"60",23,4,97,54,2254,23.5,72,2,"volkswagen type 3"
|
||||||
|
"61",20,4,140,90,2408,19.5,72,1,"chevrolet vega"
|
||||||
|
"62",21,4,122,86,2226,16.5,72,1,"ford pinto runabout"
|
||||||
|
"63",13,8,350,165,4274,12,72,1,"chevrolet impala"
|
||||||
|
"64",14,8,400,175,4385,12,72,1,"pontiac catalina"
|
||||||
|
"65",15,8,318,150,4135,13.5,72,1,"plymouth fury iii"
|
||||||
|
"66",14,8,351,153,4129,13,72,1,"ford galaxie 500"
|
||||||
|
"67",17,8,304,150,3672,11.5,72,1,"amc ambassador sst"
|
||||||
|
"68",11,8,429,208,4633,11,72,1,"mercury marquis"
|
||||||
|
"69",13,8,350,155,4502,13.5,72,1,"buick lesabre custom"
|
||||||
|
"70",12,8,350,160,4456,13.5,72,1,"oldsmobile delta 88 royale"
|
||||||
|
"71",13,8,400,190,4422,12.5,72,1,"chrysler newport royal"
|
||||||
|
"72",19,3,70,97,2330,13.5,72,3,"mazda rx2 coupe"
|
||||||
|
"73",15,8,304,150,3892,12.5,72,1,"amc matador (sw)"
|
||||||
|
"74",13,8,307,130,4098,14,72,1,"chevrolet chevelle concours (sw)"
|
||||||
|
"75",13,8,302,140,4294,16,72,1,"ford gran torino (sw)"
|
||||||
|
"76",14,8,318,150,4077,14,72,1,"plymouth satellite custom (sw)"
|
||||||
|
"77",18,4,121,112,2933,14.5,72,2,"volvo 145e (sw)"
|
||||||
|
"78",22,4,121,76,2511,18,72,2,"volkswagen 411 (sw)"
|
||||||
|
"79",21,4,120,87,2979,19.5,72,2,"peugeot 504 (sw)"
|
||||||
|
"80",26,4,96,69,2189,18,72,2,"renault 12 (sw)"
|
||||||
|
"81",22,4,122,86,2395,16,72,1,"ford pinto (sw)"
|
||||||
|
"82",28,4,97,92,2288,17,72,3,"datsun 510 (sw)"
|
||||||
|
"83",23,4,120,97,2506,14.5,72,3,"toyouta corona mark ii (sw)"
|
||||||
|
"84",28,4,98,80,2164,15,72,1,"dodge colt (sw)"
|
||||||
|
"85",27,4,97,88,2100,16.5,72,3,"toyota corolla 1600 (sw)"
|
||||||
|
"86",13,8,350,175,4100,13,73,1,"buick century 350"
|
||||||
|
"87",14,8,304,150,3672,11.5,73,1,"amc matador"
|
||||||
|
"88",13,8,350,145,3988,13,73,1,"chevrolet malibu"
|
||||||
|
"89",14,8,302,137,4042,14.5,73,1,"ford gran torino"
|
||||||
|
"90",15,8,318,150,3777,12.5,73,1,"dodge coronet custom"
|
||||||
|
"91",12,8,429,198,4952,11.5,73,1,"mercury marquis brougham"
|
||||||
|
"92",13,8,400,150,4464,12,73,1,"chevrolet caprice classic"
|
||||||
|
"93",13,8,351,158,4363,13,73,1,"ford ltd"
|
||||||
|
"94",14,8,318,150,4237,14.5,73,1,"plymouth fury gran sedan"
|
||||||
|
"95",13,8,440,215,4735,11,73,1,"chrysler new yorker brougham"
|
||||||
|
"96",12,8,455,225,4951,11,73,1,"buick electra 225 custom"
|
||||||
|
"97",13,8,360,175,3821,11,73,1,"amc ambassador brougham"
|
||||||
|
"98",18,6,225,105,3121,16.5,73,1,"plymouth valiant"
|
||||||
|
"99",16,6,250,100,3278,18,73,1,"chevrolet nova custom"
|
||||||
|
"100",18,6,232,100,2945,16,73,1,"amc hornet"
|
||||||
|
"101",18,6,250,88,3021,16.5,73,1,"ford maverick"
|
||||||
|
"102",23,6,198,95,2904,16,73,1,"plymouth duster"
|
||||||
|
"103",26,4,97,46,1950,21,73,2,"volkswagen super beetle"
|
||||||
|
"104",11,8,400,150,4997,14,73,1,"chevrolet impala"
|
||||||
|
"105",12,8,400,167,4906,12.5,73,1,"ford country"
|
||||||
|
"106",13,8,360,170,4654,13,73,1,"plymouth custom suburb"
|
||||||
|
"107",12,8,350,180,4499,12.5,73,1,"oldsmobile vista cruiser"
|
||||||
|
"108",18,6,232,100,2789,15,73,1,"amc gremlin"
|
||||||
|
"109",20,4,97,88,2279,19,73,3,"toyota carina"
|
||||||
|
"110",21,4,140,72,2401,19.5,73,1,"chevrolet vega"
|
||||||
|
"111",22,4,108,94,2379,16.5,73,3,"datsun 610"
|
||||||
|
"112",18,3,70,90,2124,13.5,73,3,"maxda rx3"
|
||||||
|
"113",19,4,122,85,2310,18.5,73,1,"ford pinto"
|
||||||
|
"114",21,6,155,107,2472,14,73,1,"mercury capri v6"
|
||||||
|
"115",26,4,98,90,2265,15.5,73,2,"fiat 124 sport coupe"
|
||||||
|
"116",15,8,350,145,4082,13,73,1,"chevrolet monte carlo s"
|
||||||
|
"117",16,8,400,230,4278,9.5,73,1,"pontiac grand prix"
|
||||||
|
"118",29,4,68,49,1867,19.5,73,2,"fiat 128"
|
||||||
|
"119",24,4,116,75,2158,15.5,73,2,"opel manta"
|
||||||
|
"120",20,4,114,91,2582,14,73,2,"audi 100ls"
|
||||||
|
"121",19,4,121,112,2868,15.5,73,2,"volvo 144ea"
|
||||||
|
"122",15,8,318,150,3399,11,73,1,"dodge dart custom"
|
||||||
|
"123",24,4,121,110,2660,14,73,2,"saab 99le"
|
||||||
|
"124",20,6,156,122,2807,13.5,73,3,"toyota mark ii"
|
||||||
|
"125",11,8,350,180,3664,11,73,1,"oldsmobile omega"
|
||||||
|
"126",20,6,198,95,3102,16.5,74,1,"plymouth duster"
|
||||||
|
"128",19,6,232,100,2901,16,74,1,"amc hornet"
|
||||||
|
"129",15,6,250,100,3336,17,74,1,"chevrolet nova"
|
||||||
|
"130",31,4,79,67,1950,19,74,3,"datsun b210"
|
||||||
|
"131",26,4,122,80,2451,16.5,74,1,"ford pinto"
|
||||||
|
"132",32,4,71,65,1836,21,74,3,"toyota corolla 1200"
|
||||||
|
"133",25,4,140,75,2542,17,74,1,"chevrolet vega"
|
||||||
|
"134",16,6,250,100,3781,17,74,1,"chevrolet chevelle malibu classic"
|
||||||
|
"135",16,6,258,110,3632,18,74,1,"amc matador"
|
||||||
|
"136",18,6,225,105,3613,16.5,74,1,"plymouth satellite sebring"
|
||||||
|
"137",16,8,302,140,4141,14,74,1,"ford gran torino"
|
||||||
|
"138",13,8,350,150,4699,14.5,74,1,"buick century luxus (sw)"
|
||||||
|
"139",14,8,318,150,4457,13.5,74,1,"dodge coronet custom (sw)"
|
||||||
|
"140",14,8,302,140,4638,16,74,1,"ford gran torino (sw)"
|
||||||
|
"141",14,8,304,150,4257,15.5,74,1,"amc matador (sw)"
|
||||||
|
"142",29,4,98,83,2219,16.5,74,2,"audi fox"
|
||||||
|
"143",26,4,79,67,1963,15.5,74,2,"volkswagen dasher"
|
||||||
|
"144",26,4,97,78,2300,14.5,74,2,"opel manta"
|
||||||
|
"145",31,4,76,52,1649,16.5,74,3,"toyota corona"
|
||||||
|
"146",32,4,83,61,2003,19,74,3,"datsun 710"
|
||||||
|
"147",28,4,90,75,2125,14.5,74,1,"dodge colt"
|
||||||
|
"148",24,4,90,75,2108,15.5,74,2,"fiat 128"
|
||||||
|
"149",26,4,116,75,2246,14,74,2,"fiat 124 tc"
|
||||||
|
"150",24,4,120,97,2489,15,74,3,"honda civic"
|
||||||
|
"151",26,4,108,93,2391,15.5,74,3,"subaru"
|
||||||
|
"152",31,4,79,67,2000,16,74,2,"fiat x1.9"
|
||||||
|
"153",19,6,225,95,3264,16,75,1,"plymouth valiant custom"
|
||||||
|
"154",18,6,250,105,3459,16,75,1,"chevrolet nova"
|
||||||
|
"155",15,6,250,72,3432,21,75,1,"mercury monarch"
|
||||||
|
"156",15,6,250,72,3158,19.5,75,1,"ford maverick"
|
||||||
|
"157",16,8,400,170,4668,11.5,75,1,"pontiac catalina"
|
||||||
|
"158",15,8,350,145,4440,14,75,1,"chevrolet bel air"
|
||||||
|
"159",16,8,318,150,4498,14.5,75,1,"plymouth grand fury"
|
||||||
|
"160",14,8,351,148,4657,13.5,75,1,"ford ltd"
|
||||||
|
"161",17,6,231,110,3907,21,75,1,"buick century"
|
||||||
|
"162",16,6,250,105,3897,18.5,75,1,"chevroelt chevelle malibu"
|
||||||
|
"163",15,6,258,110,3730,19,75,1,"amc matador"
|
||||||
|
"164",18,6,225,95,3785,19,75,1,"plymouth fury"
|
||||||
|
"165",21,6,231,110,3039,15,75,1,"buick skyhawk"
|
||||||
|
"166",20,8,262,110,3221,13.5,75,1,"chevrolet monza 2+2"
|
||||||
|
"167",13,8,302,129,3169,12,75,1,"ford mustang ii"
|
||||||
|
"168",29,4,97,75,2171,16,75,3,"toyota corolla"
|
||||||
|
"169",23,4,140,83,2639,17,75,1,"ford pinto"
|
||||||
|
"170",20,6,232,100,2914,16,75,1,"amc gremlin"
|
||||||
|
"171",23,4,140,78,2592,18.5,75,1,"pontiac astro"
|
||||||
|
"172",24,4,134,96,2702,13.5,75,3,"toyota corona"
|
||||||
|
"173",25,4,90,71,2223,16.5,75,2,"volkswagen dasher"
|
||||||
|
"174",24,4,119,97,2545,17,75,3,"datsun 710"
|
||||||
|
"175",18,6,171,97,2984,14.5,75,1,"ford pinto"
|
||||||
|
"176",29,4,90,70,1937,14,75,2,"volkswagen rabbit"
|
||||||
|
"177",19,6,232,90,3211,17,75,1,"amc pacer"
|
||||||
|
"178",23,4,115,95,2694,15,75,2,"audi 100ls"
|
||||||
|
"179",23,4,120,88,2957,17,75,2,"peugeot 504"
|
||||||
|
"180",22,4,121,98,2945,14.5,75,2,"volvo 244dl"
|
||||||
|
"181",25,4,121,115,2671,13.5,75,2,"saab 99le"
|
||||||
|
"182",33,4,91,53,1795,17.5,75,3,"honda civic cvcc"
|
||||||
|
"183",28,4,107,86,2464,15.5,76,2,"fiat 131"
|
||||||
|
"184",25,4,116,81,2220,16.9,76,2,"opel 1900"
|
||||||
|
"185",25,4,140,92,2572,14.9,76,1,"capri ii"
|
||||||
|
"186",26,4,98,79,2255,17.7,76,1,"dodge colt"
|
||||||
|
"187",27,4,101,83,2202,15.3,76,2,"renault 12tl"
|
||||||
|
"188",17.5,8,305,140,4215,13,76,1,"chevrolet chevelle malibu classic"
|
||||||
|
"189",16,8,318,150,4190,13,76,1,"dodge coronet brougham"
|
||||||
|
"190",15.5,8,304,120,3962,13.9,76,1,"amc matador"
|
||||||
|
"191",14.5,8,351,152,4215,12.8,76,1,"ford gran torino"
|
||||||
|
"192",22,6,225,100,3233,15.4,76,1,"plymouth valiant"
|
||||||
|
"193",22,6,250,105,3353,14.5,76,1,"chevrolet nova"
|
||||||
|
"194",24,6,200,81,3012,17.6,76,1,"ford maverick"
|
||||||
|
"195",22.5,6,232,90,3085,17.6,76,1,"amc hornet"
|
||||||
|
"196",29,4,85,52,2035,22.2,76,1,"chevrolet chevette"
|
||||||
|
"197",24.5,4,98,60,2164,22.1,76,1,"chevrolet woody"
|
||||||
|
"198",29,4,90,70,1937,14.2,76,2,"vw rabbit"
|
||||||
|
"199",33,4,91,53,1795,17.4,76,3,"honda civic"
|
||||||
|
"200",20,6,225,100,3651,17.7,76,1,"dodge aspen se"
|
||||||
|
"201",18,6,250,78,3574,21,76,1,"ford granada ghia"
|
||||||
|
"202",18.5,6,250,110,3645,16.2,76,1,"pontiac ventura sj"
|
||||||
|
"203",17.5,6,258,95,3193,17.8,76,1,"amc pacer d/l"
|
||||||
|
"204",29.5,4,97,71,1825,12.2,76,2,"volkswagen rabbit"
|
||||||
|
"205",32,4,85,70,1990,17,76,3,"datsun b-210"
|
||||||
|
"206",28,4,97,75,2155,16.4,76,3,"toyota corolla"
|
||||||
|
"207",26.5,4,140,72,2565,13.6,76,1,"ford pinto"
|
||||||
|
"208",20,4,130,102,3150,15.7,76,2,"volvo 245"
|
||||||
|
"209",13,8,318,150,3940,13.2,76,1,"plymouth volare premier v8"
|
||||||
|
"210",19,4,120,88,3270,21.9,76,2,"peugeot 504"
|
||||||
|
"211",19,6,156,108,2930,15.5,76,3,"toyota mark ii"
|
||||||
|
"212",16.5,6,168,120,3820,16.7,76,2,"mercedes-benz 280s"
|
||||||
|
"213",16.5,8,350,180,4380,12.1,76,1,"cadillac seville"
|
||||||
|
"214",13,8,350,145,4055,12,76,1,"chevy c10"
|
||||||
|
"215",13,8,302,130,3870,15,76,1,"ford f108"
|
||||||
|
"216",13,8,318,150,3755,14,76,1,"dodge d100"
|
||||||
|
"217",31.5,4,98,68,2045,18.5,77,3,"honda accord cvcc"
|
||||||
|
"218",30,4,111,80,2155,14.8,77,1,"buick opel isuzu deluxe"
|
||||||
|
"219",36,4,79,58,1825,18.6,77,2,"renault 5 gtl"
|
||||||
|
"220",25.5,4,122,96,2300,15.5,77,1,"plymouth arrow gs"
|
||||||
|
"221",33.5,4,85,70,1945,16.8,77,3,"datsun f-10 hatchback"
|
||||||
|
"222",17.5,8,305,145,3880,12.5,77,1,"chevrolet caprice classic"
|
||||||
|
"223",17,8,260,110,4060,19,77,1,"oldsmobile cutlass supreme"
|
||||||
|
"224",15.5,8,318,145,4140,13.7,77,1,"dodge monaco brougham"
|
||||||
|
"225",15,8,302,130,4295,14.9,77,1,"mercury cougar brougham"
|
||||||
|
"226",17.5,6,250,110,3520,16.4,77,1,"chevrolet concours"
|
||||||
|
"227",20.5,6,231,105,3425,16.9,77,1,"buick skylark"
|
||||||
|
"228",19,6,225,100,3630,17.7,77,1,"plymouth volare custom"
|
||||||
|
"229",18.5,6,250,98,3525,19,77,1,"ford granada"
|
||||||
|
"230",16,8,400,180,4220,11.1,77,1,"pontiac grand prix lj"
|
||||||
|
"231",15.5,8,350,170,4165,11.4,77,1,"chevrolet monte carlo landau"
|
||||||
|
"232",15.5,8,400,190,4325,12.2,77,1,"chrysler cordoba"
|
||||||
|
"233",16,8,351,149,4335,14.5,77,1,"ford thunderbird"
|
||||||
|
"234",29,4,97,78,1940,14.5,77,2,"volkswagen rabbit custom"
|
||||||
|
"235",24.5,4,151,88,2740,16,77,1,"pontiac sunbird coupe"
|
||||||
|
"236",26,4,97,75,2265,18.2,77,3,"toyota corolla liftback"
|
||||||
|
"237",25.5,4,140,89,2755,15.8,77,1,"ford mustang ii 2+2"
|
||||||
|
"238",30.5,4,98,63,2051,17,77,1,"chevrolet chevette"
|
||||||
|
"239",33.5,4,98,83,2075,15.9,77,1,"dodge colt m/m"
|
||||||
|
"240",30,4,97,67,1985,16.4,77,3,"subaru dl"
|
||||||
|
"241",30.5,4,97,78,2190,14.1,77,2,"volkswagen dasher"
|
||||||
|
"242",22,6,146,97,2815,14.5,77,3,"datsun 810"
|
||||||
|
"243",21.5,4,121,110,2600,12.8,77,2,"bmw 320i"
|
||||||
|
"244",21.5,3,80,110,2720,13.5,77,3,"mazda rx-4"
|
||||||
|
"245",43.1,4,90,48,1985,21.5,78,2,"volkswagen rabbit custom diesel"
|
||||||
|
"246",36.1,4,98,66,1800,14.4,78,1,"ford fiesta"
|
||||||
|
"247",32.8,4,78,52,1985,19.4,78,3,"mazda glc deluxe"
|
||||||
|
"248",39.4,4,85,70,2070,18.6,78,3,"datsun b210 gx"
|
||||||
|
"249",36.1,4,91,60,1800,16.4,78,3,"honda civic cvcc"
|
||||||
|
"250",19.9,8,260,110,3365,15.5,78,1,"oldsmobile cutlass salon brougham"
|
||||||
|
"251",19.4,8,318,140,3735,13.2,78,1,"dodge diplomat"
|
||||||
|
"252",20.2,8,302,139,3570,12.8,78,1,"mercury monarch ghia"
|
||||||
|
"253",19.2,6,231,105,3535,19.2,78,1,"pontiac phoenix lj"
|
||||||
|
"254",20.5,6,200,95,3155,18.2,78,1,"chevrolet malibu"
|
||||||
|
"255",20.2,6,200,85,2965,15.8,78,1,"ford fairmont (auto)"
|
||||||
|
"256",25.1,4,140,88,2720,15.4,78,1,"ford fairmont (man)"
|
||||||
|
"257",20.5,6,225,100,3430,17.2,78,1,"plymouth volare"
|
||||||
|
"258",19.4,6,232,90,3210,17.2,78,1,"amc concord"
|
||||||
|
"259",20.6,6,231,105,3380,15.8,78,1,"buick century special"
|
||||||
|
"260",20.8,6,200,85,3070,16.7,78,1,"mercury zephyr"
|
||||||
|
"261",18.6,6,225,110,3620,18.7,78,1,"dodge aspen"
|
||||||
|
"262",18.1,6,258,120,3410,15.1,78,1,"amc concord d/l"
|
||||||
|
"263",19.2,8,305,145,3425,13.2,78,1,"chevrolet monte carlo landau"
|
||||||
|
"264",17.7,6,231,165,3445,13.4,78,1,"buick regal sport coupe (turbo)"
|
||||||
|
"265",18.1,8,302,139,3205,11.2,78,1,"ford futura"
|
||||||
|
"266",17.5,8,318,140,4080,13.7,78,1,"dodge magnum xe"
|
||||||
|
"267",30,4,98,68,2155,16.5,78,1,"chevrolet chevette"
|
||||||
|
"268",27.5,4,134,95,2560,14.2,78,3,"toyota corona"
|
||||||
|
"269",27.2,4,119,97,2300,14.7,78,3,"datsun 510"
|
||||||
|
"270",30.9,4,105,75,2230,14.5,78,1,"dodge omni"
|
||||||
|
"271",21.1,4,134,95,2515,14.8,78,3,"toyota celica gt liftback"
|
||||||
|
"272",23.2,4,156,105,2745,16.7,78,1,"plymouth sapporo"
|
||||||
|
"273",23.8,4,151,85,2855,17.6,78,1,"oldsmobile starfire sx"
|
||||||
|
"274",23.9,4,119,97,2405,14.9,78,3,"datsun 200-sx"
|
||||||
|
"275",20.3,5,131,103,2830,15.9,78,2,"audi 5000"
|
||||||
|
"276",17,6,163,125,3140,13.6,78,2,"volvo 264gl"
|
||||||
|
"277",21.6,4,121,115,2795,15.7,78,2,"saab 99gle"
|
||||||
|
"278",16.2,6,163,133,3410,15.8,78,2,"peugeot 604sl"
|
||||||
|
"279",31.5,4,89,71,1990,14.9,78,2,"volkswagen scirocco"
|
||||||
|
"280",29.5,4,98,68,2135,16.6,78,3,"honda accord lx"
|
||||||
|
"281",21.5,6,231,115,3245,15.4,79,1,"pontiac lemans v6"
|
||||||
|
"282",19.8,6,200,85,2990,18.2,79,1,"mercury zephyr 6"
|
||||||
|
"283",22.3,4,140,88,2890,17.3,79,1,"ford fairmont 4"
|
||||||
|
"284",20.2,6,232,90,3265,18.2,79,1,"amc concord dl 6"
|
||||||
|
"285",20.6,6,225,110,3360,16.6,79,1,"dodge aspen 6"
|
||||||
|
"286",17,8,305,130,3840,15.4,79,1,"chevrolet caprice classic"
|
||||||
|
"287",17.6,8,302,129,3725,13.4,79,1,"ford ltd landau"
|
||||||
|
"288",16.5,8,351,138,3955,13.2,79,1,"mercury grand marquis"
|
||||||
|
"289",18.2,8,318,135,3830,15.2,79,1,"dodge st. regis"
|
||||||
|
"290",16.9,8,350,155,4360,14.9,79,1,"buick estate wagon (sw)"
|
||||||
|
"291",15.5,8,351,142,4054,14.3,79,1,"ford country squire (sw)"
|
||||||
|
"292",19.2,8,267,125,3605,15,79,1,"chevrolet malibu classic (sw)"
|
||||||
|
"293",18.5,8,360,150,3940,13,79,1,"chrysler lebaron town @ country (sw)"
|
||||||
|
"294",31.9,4,89,71,1925,14,79,2,"vw rabbit custom"
|
||||||
|
"295",34.1,4,86,65,1975,15.2,79,3,"maxda glc deluxe"
|
||||||
|
"296",35.7,4,98,80,1915,14.4,79,1,"dodge colt hatchback custom"
|
||||||
|
"297",27.4,4,121,80,2670,15,79,1,"amc spirit dl"
|
||||||
|
"298",25.4,5,183,77,3530,20.1,79,2,"mercedes benz 300d"
|
||||||
|
"299",23,8,350,125,3900,17.4,79,1,"cadillac eldorado"
|
||||||
|
"300",27.2,4,141,71,3190,24.8,79,2,"peugeot 504"
|
||||||
|
"301",23.9,8,260,90,3420,22.2,79,1,"oldsmobile cutlass salon brougham"
|
||||||
|
"302",34.2,4,105,70,2200,13.2,79,1,"plymouth horizon"
|
||||||
|
"303",34.5,4,105,70,2150,14.9,79,1,"plymouth horizon tc3"
|
||||||
|
"304",31.8,4,85,65,2020,19.2,79,3,"datsun 210"
|
||||||
|
"305",37.3,4,91,69,2130,14.7,79,2,"fiat strada custom"
|
||||||
|
"306",28.4,4,151,90,2670,16,79,1,"buick skylark limited"
|
||||||
|
"307",28.8,6,173,115,2595,11.3,79,1,"chevrolet citation"
|
||||||
|
"308",26.8,6,173,115,2700,12.9,79,1,"oldsmobile omega brougham"
|
||||||
|
"309",33.5,4,151,90,2556,13.2,79,1,"pontiac phoenix"
|
||||||
|
"310",41.5,4,98,76,2144,14.7,80,2,"vw rabbit"
|
||||||
|
"311",38.1,4,89,60,1968,18.8,80,3,"toyota corolla tercel"
|
||||||
|
"312",32.1,4,98,70,2120,15.5,80,1,"chevrolet chevette"
|
||||||
|
"313",37.2,4,86,65,2019,16.4,80,3,"datsun 310"
|
||||||
|
"314",28,4,151,90,2678,16.5,80,1,"chevrolet citation"
|
||||||
|
"315",26.4,4,140,88,2870,18.1,80,1,"ford fairmont"
|
||||||
|
"316",24.3,4,151,90,3003,20.1,80,1,"amc concord"
|
||||||
|
"317",19.1,6,225,90,3381,18.7,80,1,"dodge aspen"
|
||||||
|
"318",34.3,4,97,78,2188,15.8,80,2,"audi 4000"
|
||||||
|
"319",29.8,4,134,90,2711,15.5,80,3,"toyota corona liftback"
|
||||||
|
"320",31.3,4,120,75,2542,17.5,80,3,"mazda 626"
|
||||||
|
"321",37,4,119,92,2434,15,80,3,"datsun 510 hatchback"
|
||||||
|
"322",32.2,4,108,75,2265,15.2,80,3,"toyota corolla"
|
||||||
|
"323",46.6,4,86,65,2110,17.9,80,3,"mazda glc"
|
||||||
|
"324",27.9,4,156,105,2800,14.4,80,1,"dodge colt"
|
||||||
|
"325",40.8,4,85,65,2110,19.2,80,3,"datsun 210"
|
||||||
|
"326",44.3,4,90,48,2085,21.7,80,2,"vw rabbit c (diesel)"
|
||||||
|
"327",43.4,4,90,48,2335,23.7,80,2,"vw dasher (diesel)"
|
||||||
|
"328",36.4,5,121,67,2950,19.9,80,2,"audi 5000s (diesel)"
|
||||||
|
"329",30,4,146,67,3250,21.8,80,2,"mercedes-benz 240d"
|
||||||
|
"330",44.6,4,91,67,1850,13.8,80,3,"honda civic 1500 gl"
|
||||||
|
"332",33.8,4,97,67,2145,18,80,3,"subaru dl"
|
||||||
|
"333",29.8,4,89,62,1845,15.3,80,2,"vokswagen rabbit"
|
||||||
|
"334",32.7,6,168,132,2910,11.4,80,3,"datsun 280-zx"
|
||||||
|
"335",23.7,3,70,100,2420,12.5,80,3,"mazda rx-7 gs"
|
||||||
|
"336",35,4,122,88,2500,15.1,80,2,"triumph tr7 coupe"
|
||||||
|
"338",32.4,4,107,72,2290,17,80,3,"honda accord"
|
||||||
|
"339",27.2,4,135,84,2490,15.7,81,1,"plymouth reliant"
|
||||||
|
"340",26.6,4,151,84,2635,16.4,81,1,"buick skylark"
|
||||||
|
"341",25.8,4,156,92,2620,14.4,81,1,"dodge aries wagon (sw)"
|
||||||
|
"342",23.5,6,173,110,2725,12.6,81,1,"chevrolet citation"
|
||||||
|
"343",30,4,135,84,2385,12.9,81,1,"plymouth reliant"
|
||||||
|
"344",39.1,4,79,58,1755,16.9,81,3,"toyota starlet"
|
||||||
|
"345",39,4,86,64,1875,16.4,81,1,"plymouth champ"
|
||||||
|
"346",35.1,4,81,60,1760,16.1,81,3,"honda civic 1300"
|
||||||
|
"347",32.3,4,97,67,2065,17.8,81,3,"subaru"
|
||||||
|
"348",37,4,85,65,1975,19.4,81,3,"datsun 210 mpg"
|
||||||
|
"349",37.7,4,89,62,2050,17.3,81,3,"toyota tercel"
|
||||||
|
"350",34.1,4,91,68,1985,16,81,3,"mazda glc 4"
|
||||||
|
"351",34.7,4,105,63,2215,14.9,81,1,"plymouth horizon 4"
|
||||||
|
"352",34.4,4,98,65,2045,16.2,81,1,"ford escort 4w"
|
||||||
|
"353",29.9,4,98,65,2380,20.7,81,1,"ford escort 2h"
|
||||||
|
"354",33,4,105,74,2190,14.2,81,2,"volkswagen jetta"
|
||||||
|
"356",33.7,4,107,75,2210,14.4,81,3,"honda prelude"
|
||||||
|
"357",32.4,4,108,75,2350,16.8,81,3,"toyota corolla"
|
||||||
|
"358",32.9,4,119,100,2615,14.8,81,3,"datsun 200sx"
|
||||||
|
"359",31.6,4,120,74,2635,18.3,81,3,"mazda 626"
|
||||||
|
"360",28.1,4,141,80,3230,20.4,81,2,"peugeot 505s turbo diesel"
|
||||||
|
"361",30.7,6,145,76,3160,19.6,81,2,"volvo diesel"
|
||||||
|
"362",25.4,6,168,116,2900,12.6,81,3,"toyota cressida"
|
||||||
|
"363",24.2,6,146,120,2930,13.8,81,3,"datsun 810 maxima"
|
||||||
|
"364",22.4,6,231,110,3415,15.8,81,1,"buick century"
|
||||||
|
"365",26.6,8,350,105,3725,19,81,1,"oldsmobile cutlass ls"
|
||||||
|
"366",20.2,6,200,88,3060,17.1,81,1,"ford granada gl"
|
||||||
|
"367",17.6,6,225,85,3465,16.6,81,1,"chrysler lebaron salon"
|
||||||
|
"368",28,4,112,88,2605,19.6,82,1,"chevrolet cavalier"
|
||||||
|
"369",27,4,112,88,2640,18.6,82,1,"chevrolet cavalier wagon"
|
||||||
|
"370",34,4,112,88,2395,18,82,1,"chevrolet cavalier 2-door"
|
||||||
|
"371",31,4,112,85,2575,16.2,82,1,"pontiac j2000 se hatchback"
|
||||||
|
"372",29,4,135,84,2525,16,82,1,"dodge aries se"
|
||||||
|
"373",27,4,151,90,2735,18,82,1,"pontiac phoenix"
|
||||||
|
"374",24,4,140,92,2865,16.4,82,1,"ford fairmont futura"
|
||||||
|
"375",36,4,105,74,1980,15.3,82,2,"volkswagen rabbit l"
|
||||||
|
"376",37,4,91,68,2025,18.2,82,3,"mazda glc custom l"
|
||||||
|
"377",31,4,91,68,1970,17.6,82,3,"mazda glc custom"
|
||||||
|
"378",38,4,105,63,2125,14.7,82,1,"plymouth horizon miser"
|
||||||
|
"379",36,4,98,70,2125,17.3,82,1,"mercury lynx l"
|
||||||
|
"380",36,4,120,88,2160,14.5,82,3,"nissan stanza xe"
|
||||||
|
"381",36,4,107,75,2205,14.5,82,3,"honda accord"
|
||||||
|
"382",34,4,108,70,2245,16.9,82,3,"toyota corolla"
|
||||||
|
"383",38,4,91,67,1965,15,82,3,"honda civic"
|
||||||
|
"384",32,4,91,67,1965,15.7,82,3,"honda civic (auto)"
|
||||||
|
"385",38,4,91,67,1995,16.2,82,3,"datsun 310 gx"
|
||||||
|
"386",25,6,181,110,2945,16.4,82,1,"buick century limited"
|
||||||
|
"387",38,6,262,85,3015,17,82,1,"oldsmobile cutlass ciera (diesel)"
|
||||||
|
"388",26,4,156,92,2585,14.5,82,1,"chrysler lebaron medallion"
|
||||||
|
"389",22,6,232,112,2835,14.7,82,1,"ford granada l"
|
||||||
|
"390",32,4,144,96,2665,13.9,82,3,"toyota celica gt"
|
||||||
|
"391",36,4,135,84,2370,13,82,1,"dodge charger 2.2"
|
||||||
|
"392",27,4,151,90,2950,17.3,82,1,"chevrolet camaro"
|
||||||
|
"393",27,4,140,86,2790,15.6,82,1,"ford mustang gl"
|
||||||
|
"394",44,4,97,52,2130,24.6,82,2,"vw pickup"
|
||||||
|
"395",32,4,135,84,2295,11.6,82,1,"dodge rampage"
|
||||||
|
"396",28,4,120,79,2625,18.6,82,1,"ford ranger"
|
||||||
|
"397",31,4,119,82,2720,19.4,82,1,"chevy s-10"
|
||||||
|
398
datasets/Auto.data
Normal file
398
datasets/Auto.data
Normal file
@ -0,0 +1,398 @@
|
|||||||
|
mpg cylinders displacement horsepower weight acceleration year origin name
|
||||||
|
18.0 8 307.0 130.0 3504. 12.0 70 1 "chevrolet chevelle malibu"
|
||||||
|
15.0 8 350.0 165.0 3693. 11.5 70 1 "buick skylark 320"
|
||||||
|
18.0 8 318.0 150.0 3436. 11.0 70 1 "plymouth satellite"
|
||||||
|
16.0 8 304.0 150.0 3433. 12.0 70 1 "amc rebel sst"
|
||||||
|
17.0 8 302.0 140.0 3449. 10.5 70 1 "ford torino"
|
||||||
|
15.0 8 429.0 198.0 4341. 10.0 70 1 "ford galaxie 500"
|
||||||
|
14.0 8 454.0 220.0 4354. 9.0 70 1 "chevrolet impala"
|
||||||
|
14.0 8 440.0 215.0 4312. 8.5 70 1 "plymouth fury iii"
|
||||||
|
14.0 8 455.0 225.0 4425. 10.0 70 1 "pontiac catalina"
|
||||||
|
15.0 8 390.0 190.0 3850. 8.5 70 1 "amc ambassador dpl"
|
||||||
|
15.0 8 383.0 170.0 3563. 10.0 70 1 "dodge challenger se"
|
||||||
|
14.0 8 340.0 160.0 3609. 8.0 70 1 "plymouth 'cuda 340"
|
||||||
|
15.0 8 400.0 150.0 3761. 9.5 70 1 "chevrolet monte carlo"
|
||||||
|
14.0 8 455.0 225.0 3086. 10.0 70 1 "buick estate wagon (sw)"
|
||||||
|
24.0 4 113.0 95.00 2372. 15.0 70 3 "toyota corona mark ii"
|
||||||
|
22.0 6 198.0 95.00 2833. 15.5 70 1 "plymouth duster"
|
||||||
|
18.0 6 199.0 97.00 2774. 15.5 70 1 "amc hornet"
|
||||||
|
21.0 6 200.0 85.00 2587. 16.0 70 1 "ford maverick"
|
||||||
|
27.0 4 97.00 88.00 2130. 14.5 70 3 "datsun pl510"
|
||||||
|
26.0 4 97.00 46.00 1835. 20.5 70 2 "volkswagen 1131 deluxe sedan"
|
||||||
|
25.0 4 110.0 87.00 2672. 17.5 70 2 "peugeot 504"
|
||||||
|
24.0 4 107.0 90.00 2430. 14.5 70 2 "audi 100 ls"
|
||||||
|
25.0 4 104.0 95.00 2375. 17.5 70 2 "saab 99e"
|
||||||
|
26.0 4 121.0 113.0 2234. 12.5 70 2 "bmw 2002"
|
||||||
|
21.0 6 199.0 90.00 2648. 15.0 70 1 "amc gremlin"
|
||||||
|
10.0 8 360.0 215.0 4615. 14.0 70 1 "ford f250"
|
||||||
|
10.0 8 307.0 200.0 4376. 15.0 70 1 "chevy c20"
|
||||||
|
11.0 8 318.0 210.0 4382. 13.5 70 1 "dodge d200"
|
||||||
|
9.0 8 304.0 193.0 4732. 18.5 70 1 "hi 1200d"
|
||||||
|
27.0 4 97.00 88.00 2130. 14.5 71 3 "datsun pl510"
|
||||||
|
28.0 4 140.0 90.00 2264. 15.5 71 1 "chevrolet vega 2300"
|
||||||
|
25.0 4 113.0 95.00 2228. 14.0 71 3 "toyota corona"
|
||||||
|
25.0 4 98.00 ? 2046. 19.0 71 1 "ford pinto"
|
||||||
|
19.0 6 232.0 100.0 2634. 13.0 71 1 "amc gremlin"
|
||||||
|
16.0 6 225.0 105.0 3439. 15.5 71 1 "plymouth satellite custom"
|
||||||
|
17.0 6 250.0 100.0 3329. 15.5 71 1 "chevrolet chevelle malibu"
|
||||||
|
19.0 6 250.0 88.00 3302. 15.5 71 1 "ford torino 500"
|
||||||
|
18.0 6 232.0 100.0 3288. 15.5 71 1 "amc matador"
|
||||||
|
14.0 8 350.0 165.0 4209. 12.0 71 1 "chevrolet impala"
|
||||||
|
14.0 8 400.0 175.0 4464. 11.5 71 1 "pontiac catalina brougham"
|
||||||
|
14.0 8 351.0 153.0 4154. 13.5 71 1 "ford galaxie 500"
|
||||||
|
14.0 8 318.0 150.0 4096. 13.0 71 1 "plymouth fury iii"
|
||||||
|
12.0 8 383.0 180.0 4955. 11.5 71 1 "dodge monaco (sw)"
|
||||||
|
13.0 8 400.0 170.0 4746. 12.0 71 1 "ford country squire (sw)"
|
||||||
|
13.0 8 400.0 175.0 5140. 12.0 71 1 "pontiac safari (sw)"
|
||||||
|
18.0 6 258.0 110.0 2962. 13.5 71 1 "amc hornet sportabout (sw)"
|
||||||
|
22.0 4 140.0 72.00 2408. 19.0 71 1 "chevrolet vega (sw)"
|
||||||
|
19.0 6 250.0 100.0 3282. 15.0 71 1 "pontiac firebird"
|
||||||
|
18.0 6 250.0 88.00 3139. 14.5 71 1 "ford mustang"
|
||||||
|
23.0 4 122.0 86.00 2220. 14.0 71 1 "mercury capri 2000"
|
||||||
|
28.0 4 116.0 90.00 2123. 14.0 71 2 "opel 1900"
|
||||||
|
30.0 4 79.00 70.00 2074. 19.5 71 2 "peugeot 304"
|
||||||
|
30.0 4 88.00 76.00 2065. 14.5 71 2 "fiat 124b"
|
||||||
|
31.0 4 71.00 65.00 1773. 19.0 71 3 "toyota corolla 1200"
|
||||||
|
35.0 4 72.00 69.00 1613. 18.0 71 3 "datsun 1200"
|
||||||
|
27.0 4 97.00 60.00 1834. 19.0 71 2 "volkswagen model 111"
|
||||||
|
26.0 4 91.00 70.00 1955. 20.5 71 1 "plymouth cricket"
|
||||||
|
24.0 4 113.0 95.00 2278. 15.5 72 3 "toyota corona hardtop"
|
||||||
|
25.0 4 97.50 80.00 2126. 17.0 72 1 "dodge colt hardtop"
|
||||||
|
23.0 4 97.00 54.00 2254. 23.5 72 2 "volkswagen type 3"
|
||||||
|
20.0 4 140.0 90.00 2408. 19.5 72 1 "chevrolet vega"
|
||||||
|
21.0 4 122.0 86.00 2226. 16.5 72 1 "ford pinto runabout"
|
||||||
|
13.0 8 350.0 165.0 4274. 12.0 72 1 "chevrolet impala"
|
||||||
|
14.0 8 400.0 175.0 4385. 12.0 72 1 "pontiac catalina"
|
||||||
|
15.0 8 318.0 150.0 4135. 13.5 72 1 "plymouth fury iii"
|
||||||
|
14.0 8 351.0 153.0 4129. 13.0 72 1 "ford galaxie 500"
|
||||||
|
17.0 8 304.0 150.0 3672. 11.5 72 1 "amc ambassador sst"
|
||||||
|
11.0 8 429.0 208.0 4633. 11.0 72 1 "mercury marquis"
|
||||||
|
13.0 8 350.0 155.0 4502. 13.5 72 1 "buick lesabre custom"
|
||||||
|
12.0 8 350.0 160.0 4456. 13.5 72 1 "oldsmobile delta 88 royale"
|
||||||
|
13.0 8 400.0 190.0 4422. 12.5 72 1 "chrysler newport royal"
|
||||||
|
19.0 3 70.00 97.00 2330. 13.5 72 3 "mazda rx2 coupe"
|
||||||
|
15.0 8 304.0 150.0 3892. 12.5 72 1 "amc matador (sw)"
|
||||||
|
13.0 8 307.0 130.0 4098. 14.0 72 1 "chevrolet chevelle concours (sw)"
|
||||||
|
13.0 8 302.0 140.0 4294. 16.0 72 1 "ford gran torino (sw)"
|
||||||
|
14.0 8 318.0 150.0 4077. 14.0 72 1 "plymouth satellite custom (sw)"
|
||||||
|
18.0 4 121.0 112.0 2933. 14.5 72 2 "volvo 145e (sw)"
|
||||||
|
22.0 4 121.0 76.00 2511. 18.0 72 2 "volkswagen 411 (sw)"
|
||||||
|
21.0 4 120.0 87.00 2979. 19.5 72 2 "peugeot 504 (sw)"
|
||||||
|
26.0 4 96.00 69.00 2189. 18.0 72 2 "renault 12 (sw)"
|
||||||
|
22.0 4 122.0 86.00 2395. 16.0 72 1 "ford pinto (sw)"
|
||||||
|
28.0 4 97.00 92.00 2288. 17.0 72 3 "datsun 510 (sw)"
|
||||||
|
23.0 4 120.0 97.00 2506. 14.5 72 3 "toyouta corona mark ii (sw)"
|
||||||
|
28.0 4 98.00 80.00 2164. 15.0 72 1 "dodge colt (sw)"
|
||||||
|
27.0 4 97.00 88.00 2100. 16.5 72 3 "toyota corolla 1600 (sw)"
|
||||||
|
13.0 8 350.0 175.0 4100. 13.0 73 1 "buick century 350"
|
||||||
|
14.0 8 304.0 150.0 3672. 11.5 73 1 "amc matador"
|
||||||
|
13.0 8 350.0 145.0 3988. 13.0 73 1 "chevrolet malibu"
|
||||||
|
14.0 8 302.0 137.0 4042. 14.5 73 1 "ford gran torino"
|
||||||
|
15.0 8 318.0 150.0 3777. 12.5 73 1 "dodge coronet custom"
|
||||||
|
12.0 8 429.0 198.0 4952. 11.5 73 1 "mercury marquis brougham"
|
||||||
|
13.0 8 400.0 150.0 4464. 12.0 73 1 "chevrolet caprice classic"
|
||||||
|
13.0 8 351.0 158.0 4363. 13.0 73 1 "ford ltd"
|
||||||
|
14.0 8 318.0 150.0 4237. 14.5 73 1 "plymouth fury gran sedan"
|
||||||
|
13.0 8 440.0 215.0 4735. 11.0 73 1 "chrysler new yorker brougham"
|
||||||
|
12.0 8 455.0 225.0 4951. 11.0 73 1 "buick electra 225 custom"
|
||||||
|
13.0 8 360.0 175.0 3821. 11.0 73 1 "amc ambassador brougham"
|
||||||
|
18.0 6 225.0 105.0 3121. 16.5 73 1 "plymouth valiant"
|
||||||
|
16.0 6 250.0 100.0 3278. 18.0 73 1 "chevrolet nova custom"
|
||||||
|
18.0 6 232.0 100.0 2945. 16.0 73 1 "amc hornet"
|
||||||
|
18.0 6 250.0 88.00 3021. 16.5 73 1 "ford maverick"
|
||||||
|
23.0 6 198.0 95.00 2904. 16.0 73 1 "plymouth duster"
|
||||||
|
26.0 4 97.00 46.00 1950. 21.0 73 2 "volkswagen super beetle"
|
||||||
|
11.0 8 400.0 150.0 4997. 14.0 73 1 "chevrolet impala"
|
||||||
|
12.0 8 400.0 167.0 4906. 12.5 73 1 "ford country"
|
||||||
|
13.0 8 360.0 170.0 4654. 13.0 73 1 "plymouth custom suburb"
|
||||||
|
12.0 8 350.0 180.0 4499. 12.5 73 1 "oldsmobile vista cruiser"
|
||||||
|
18.0 6 232.0 100.0 2789. 15.0 73 1 "amc gremlin"
|
||||||
|
20.0 4 97.00 88.00 2279. 19.0 73 3 "toyota carina"
|
||||||
|
21.0 4 140.0 72.00 2401. 19.5 73 1 "chevrolet vega"
|
||||||
|
22.0 4 108.0 94.00 2379. 16.5 73 3 "datsun 610"
|
||||||
|
18.0 3 70.00 90.00 2124. 13.5 73 3 "maxda rx3"
|
||||||
|
19.0 4 122.0 85.00 2310. 18.5 73 1 "ford pinto"
|
||||||
|
21.0 6 155.0 107.0 2472. 14.0 73 1 "mercury capri v6"
|
||||||
|
26.0 4 98.00 90.00 2265. 15.5 73 2 "fiat 124 sport coupe"
|
||||||
|
15.0 8 350.0 145.0 4082. 13.0 73 1 "chevrolet monte carlo s"
|
||||||
|
16.0 8 400.0 230.0 4278. 9.50 73 1 "pontiac grand prix"
|
||||||
|
29.0 4 68.00 49.00 1867. 19.5 73 2 "fiat 128"
|
||||||
|
24.0 4 116.0 75.00 2158. 15.5 73 2 "opel manta"
|
||||||
|
20.0 4 114.0 91.00 2582. 14.0 73 2 "audi 100ls"
|
||||||
|
19.0 4 121.0 112.0 2868. 15.5 73 2 "volvo 144ea"
|
||||||
|
15.0 8 318.0 150.0 3399. 11.0 73 1 "dodge dart custom"
|
||||||
|
24.0 4 121.0 110.0 2660. 14.0 73 2 "saab 99le"
|
||||||
|
20.0 6 156.0 122.0 2807. 13.5 73 3 "toyota mark ii"
|
||||||
|
11.0 8 350.0 180.0 3664. 11.0 73 1 "oldsmobile omega"
|
||||||
|
20.0 6 198.0 95.00 3102. 16.5 74 1 "plymouth duster"
|
||||||
|
21.0 6 200.0 ? 2875. 17.0 74 1 "ford maverick"
|
||||||
|
19.0 6 232.0 100.0 2901. 16.0 74 1 "amc hornet"
|
||||||
|
15.0 6 250.0 100.0 3336. 17.0 74 1 "chevrolet nova"
|
||||||
|
31.0 4 79.00 67.00 1950. 19.0 74 3 "datsun b210"
|
||||||
|
26.0 4 122.0 80.00 2451. 16.5 74 1 "ford pinto"
|
||||||
|
32.0 4 71.00 65.00 1836. 21.0 74 3 "toyota corolla 1200"
|
||||||
|
25.0 4 140.0 75.00 2542. 17.0 74 1 "chevrolet vega"
|
||||||
|
16.0 6 250.0 100.0 3781. 17.0 74 1 "chevrolet chevelle malibu classic"
|
||||||
|
16.0 6 258.0 110.0 3632. 18.0 74 1 "amc matador"
|
||||||
|
18.0 6 225.0 105.0 3613. 16.5 74 1 "plymouth satellite sebring"
|
||||||
|
16.0 8 302.0 140.0 4141. 14.0 74 1 "ford gran torino"
|
||||||
|
13.0 8 350.0 150.0 4699. 14.5 74 1 "buick century luxus (sw)"
|
||||||
|
14.0 8 318.0 150.0 4457. 13.5 74 1 "dodge coronet custom (sw)"
|
||||||
|
14.0 8 302.0 140.0 4638. 16.0 74 1 "ford gran torino (sw)"
|
||||||
|
14.0 8 304.0 150.0 4257. 15.5 74 1 "amc matador (sw)"
|
||||||
|
29.0 4 98.00 83.00 2219. 16.5 74 2 "audi fox"
|
||||||
|
26.0 4 79.00 67.00 1963. 15.5 74 2 "volkswagen dasher"
|
||||||
|
26.0 4 97.00 78.00 2300. 14.5 74 2 "opel manta"
|
||||||
|
31.0 4 76.00 52.00 1649. 16.5 74 3 "toyota corona"
|
||||||
|
32.0 4 83.00 61.00 2003. 19.0 74 3 "datsun 710"
|
||||||
|
28.0 4 90.00 75.00 2125. 14.5 74 1 "dodge colt"
|
||||||
|
24.0 4 90.00 75.00 2108. 15.5 74 2 "fiat 128"
|
||||||
|
26.0 4 116.0 75.00 2246. 14.0 74 2 "fiat 124 tc"
|
||||||
|
24.0 4 120.0 97.00 2489. 15.0 74 3 "honda civic"
|
||||||
|
26.0 4 108.0 93.00 2391. 15.5 74 3 "subaru"
|
||||||
|
31.0 4 79.00 67.00 2000. 16.0 74 2 "fiat x1.9"
|
||||||
|
19.0 6 225.0 95.00 3264. 16.0 75 1 "plymouth valiant custom"
|
||||||
|
18.0 6 250.0 105.0 3459. 16.0 75 1 "chevrolet nova"
|
||||||
|
15.0 6 250.0 72.00 3432. 21.0 75 1 "mercury monarch"
|
||||||
|
15.0 6 250.0 72.00 3158. 19.5 75 1 "ford maverick"
|
||||||
|
16.0 8 400.0 170.0 4668. 11.5 75 1 "pontiac catalina"
|
||||||
|
15.0 8 350.0 145.0 4440. 14.0 75 1 "chevrolet bel air"
|
||||||
|
16.0 8 318.0 150.0 4498. 14.5 75 1 "plymouth grand fury"
|
||||||
|
14.0 8 351.0 148.0 4657. 13.5 75 1 "ford ltd"
|
||||||
|
17.0 6 231.0 110.0 3907. 21.0 75 1 "buick century"
|
||||||
|
16.0 6 250.0 105.0 3897. 18.5 75 1 "chevroelt chevelle malibu"
|
||||||
|
15.0 6 258.0 110.0 3730. 19.0 75 1 "amc matador"
|
||||||
|
18.0 6 225.0 95.00 3785. 19.0 75 1 "plymouth fury"
|
||||||
|
21.0 6 231.0 110.0 3039. 15.0 75 1 "buick skyhawk"
|
||||||
|
20.0 8 262.0 110.0 3221. 13.5 75 1 "chevrolet monza 2+2"
|
||||||
|
13.0 8 302.0 129.0 3169. 12.0 75 1 "ford mustang ii"
|
||||||
|
29.0 4 97.00 75.00 2171. 16.0 75 3 "toyota corolla"
|
||||||
|
23.0 4 140.0 83.00 2639. 17.0 75 1 "ford pinto"
|
||||||
|
20.0 6 232.0 100.0 2914. 16.0 75 1 "amc gremlin"
|
||||||
|
23.0 4 140.0 78.00 2592. 18.5 75 1 "pontiac astro"
|
||||||
|
24.0 4 134.0 96.00 2702. 13.5 75 3 "toyota corona"
|
||||||
|
25.0 4 90.00 71.00 2223. 16.5 75 2 "volkswagen dasher"
|
||||||
|
24.0 4 119.0 97.00 2545. 17.0 75 3 "datsun 710"
|
||||||
|
18.0 6 171.0 97.00 2984. 14.5 75 1 "ford pinto"
|
||||||
|
29.0 4 90.00 70.00 1937. 14.0 75 2 "volkswagen rabbit"
|
||||||
|
19.0 6 232.0 90.00 3211. 17.0 75 1 "amc pacer"
|
||||||
|
23.0 4 115.0 95.00 2694. 15.0 75 2 "audi 100ls"
|
||||||
|
23.0 4 120.0 88.00 2957. 17.0 75 2 "peugeot 504"
|
||||||
|
22.0 4 121.0 98.00 2945. 14.5 75 2 "volvo 244dl"
|
||||||
|
25.0 4 121.0 115.0 2671. 13.5 75 2 "saab 99le"
|
||||||
|
33.0 4 91.00 53.00 1795. 17.5 75 3 "honda civic cvcc"
|
||||||
|
28.0 4 107.0 86.00 2464. 15.5 76 2 "fiat 131"
|
||||||
|
25.0 4 116.0 81.00 2220. 16.9 76 2 "opel 1900"
|
||||||
|
25.0 4 140.0 92.00 2572. 14.9 76 1 "capri ii"
|
||||||
|
26.0 4 98.00 79.00 2255. 17.7 76 1 "dodge colt"
|
||||||
|
27.0 4 101.0 83.00 2202. 15.3 76 2 "renault 12tl"
|
||||||
|
17.5 8 305.0 140.0 4215. 13.0 76 1 "chevrolet chevelle malibu classic"
|
||||||
|
16.0 8 318.0 150.0 4190. 13.0 76 1 "dodge coronet brougham"
|
||||||
|
15.5 8 304.0 120.0 3962. 13.9 76 1 "amc matador"
|
||||||
|
14.5 8 351.0 152.0 4215. 12.8 76 1 "ford gran torino"
|
||||||
|
22.0 6 225.0 100.0 3233. 15.4 76 1 "plymouth valiant"
|
||||||
|
22.0 6 250.0 105.0 3353. 14.5 76 1 "chevrolet nova"
|
||||||
|
24.0 6 200.0 81.00 3012. 17.6 76 1 "ford maverick"
|
||||||
|
22.5 6 232.0 90.00 3085. 17.6 76 1 "amc hornet"
|
||||||
|
29.0 4 85.00 52.00 2035. 22.2 76 1 "chevrolet chevette"
|
||||||
|
24.5 4 98.00 60.00 2164. 22.1 76 1 "chevrolet woody"
|
||||||
|
29.0 4 90.00 70.00 1937. 14.2 76 2 "vw rabbit"
|
||||||
|
33.0 4 91.00 53.00 1795. 17.4 76 3 "honda civic"
|
||||||
|
20.0 6 225.0 100.0 3651. 17.7 76 1 "dodge aspen se"
|
||||||
|
18.0 6 250.0 78.00 3574. 21.0 76 1 "ford granada ghia"
|
||||||
|
18.5 6 250.0 110.0 3645. 16.2 76 1 "pontiac ventura sj"
|
||||||
|
17.5 6 258.0 95.00 3193. 17.8 76 1 "amc pacer d/l"
|
||||||
|
29.5 4 97.00 71.00 1825. 12.2 76 2 "volkswagen rabbit"
|
||||||
|
32.0 4 85.00 70.00 1990. 17.0 76 3 "datsun b-210"
|
||||||
|
28.0 4 97.00 75.00 2155. 16.4 76 3 "toyota corolla"
|
||||||
|
26.5 4 140.0 72.00 2565. 13.6 76 1 "ford pinto"
|
||||||
|
20.0 4 130.0 102.0 3150. 15.7 76 2 "volvo 245"
|
||||||
|
13.0 8 318.0 150.0 3940. 13.2 76 1 "plymouth volare premier v8"
|
||||||
|
19.0 4 120.0 88.00 3270. 21.9 76 2 "peugeot 504"
|
||||||
|
19.0 6 156.0 108.0 2930. 15.5 76 3 "toyota mark ii"
|
||||||
|
16.5 6 168.0 120.0 3820. 16.7 76 2 "mercedes-benz 280s"
|
||||||
|
16.5 8 350.0 180.0 4380. 12.1 76 1 "cadillac seville"
|
||||||
|
13.0 8 350.0 145.0 4055. 12.0 76 1 "chevy c10"
|
||||||
|
13.0 8 302.0 130.0 3870. 15.0 76 1 "ford f108"
|
||||||
|
13.0 8 318.0 150.0 3755. 14.0 76 1 "dodge d100"
|
||||||
|
31.5 4 98.00 68.00 2045. 18.5 77 3 "honda accord cvcc"
|
||||||
|
30.0 4 111.0 80.00 2155. 14.8 77 1 "buick opel isuzu deluxe"
|
||||||
|
36.0 4 79.00 58.00 1825. 18.6 77 2 "renault 5 gtl"
|
||||||
|
25.5 4 122.0 96.00 2300. 15.5 77 1 "plymouth arrow gs"
|
||||||
|
33.5 4 85.00 70.00 1945. 16.8 77 3 "datsun f-10 hatchback"
|
||||||
|
17.5 8 305.0 145.0 3880. 12.5 77 1 "chevrolet caprice classic"
|
||||||
|
17.0 8 260.0 110.0 4060. 19.0 77 1 "oldsmobile cutlass supreme"
|
||||||
|
15.5 8 318.0 145.0 4140. 13.7 77 1 "dodge monaco brougham"
|
||||||
|
15.0 8 302.0 130.0 4295. 14.9 77 1 "mercury cougar brougham"
|
||||||
|
17.5 6 250.0 110.0 3520. 16.4 77 1 "chevrolet concours"
|
||||||
|
20.5 6 231.0 105.0 3425. 16.9 77 1 "buick skylark"
|
||||||
|
19.0 6 225.0 100.0 3630. 17.7 77 1 "plymouth volare custom"
|
||||||
|
18.5 6 250.0 98.00 3525. 19.0 77 1 "ford granada"
|
||||||
|
16.0 8 400.0 180.0 4220. 11.1 77 1 "pontiac grand prix lj"
|
||||||
|
15.5 8 350.0 170.0 4165. 11.4 77 1 "chevrolet monte carlo landau"
|
||||||
|
15.5 8 400.0 190.0 4325. 12.2 77 1 "chrysler cordoba"
|
||||||
|
16.0 8 351.0 149.0 4335. 14.5 77 1 "ford thunderbird"
|
||||||
|
29.0 4 97.00 78.00 1940. 14.5 77 2 "volkswagen rabbit custom"
|
||||||
|
24.5 4 151.0 88.00 2740. 16.0 77 1 "pontiac sunbird coupe"
|
||||||
|
26.0 4 97.00 75.00 2265. 18.2 77 3 "toyota corolla liftback"
|
||||||
|
25.5 4 140.0 89.00 2755. 15.8 77 1 "ford mustang ii 2+2"
|
||||||
|
30.5 4 98.00 63.00 2051. 17.0 77 1 "chevrolet chevette"
|
||||||
|
33.5 4 98.00 83.00 2075. 15.9 77 1 "dodge colt m/m"
|
||||||
|
30.0 4 97.00 67.00 1985. 16.4 77 3 "subaru dl"
|
||||||
|
30.5 4 97.00 78.00 2190. 14.1 77 2 "volkswagen dasher"
|
||||||
|
22.0 6 146.0 97.00 2815. 14.5 77 3 "datsun 810"
|
||||||
|
21.5 4 121.0 110.0 2600. 12.8 77 2 "bmw 320i"
|
||||||
|
21.5 3 80.00 110.0 2720. 13.5 77 3 "mazda rx-4"
|
||||||
|
43.1 4 90.00 48.00 1985. 21.5 78 2 "volkswagen rabbit custom diesel"
|
||||||
|
36.1 4 98.00 66.00 1800. 14.4 78 1 "ford fiesta"
|
||||||
|
32.8 4 78.00 52.00 1985. 19.4 78 3 "mazda glc deluxe"
|
||||||
|
39.4 4 85.00 70.00 2070. 18.6 78 3 "datsun b210 gx"
|
||||||
|
36.1 4 91.00 60.00 1800. 16.4 78 3 "honda civic cvcc"
|
||||||
|
19.9 8 260.0 110.0 3365. 15.5 78 1 "oldsmobile cutlass salon brougham"
|
||||||
|
19.4 8 318.0 140.0 3735. 13.2 78 1 "dodge diplomat"
|
||||||
|
20.2 8 302.0 139.0 3570. 12.8 78 1 "mercury monarch ghia"
|
||||||
|
19.2 6 231.0 105.0 3535. 19.2 78 1 "pontiac phoenix lj"
|
||||||
|
20.5 6 200.0 95.00 3155. 18.2 78 1 "chevrolet malibu"
|
||||||
|
20.2 6 200.0 85.00 2965. 15.8 78 1 "ford fairmont (auto)"
|
||||||
|
25.1 4 140.0 88.00 2720. 15.4 78 1 "ford fairmont (man)"
|
||||||
|
20.5 6 225.0 100.0 3430. 17.2 78 1 "plymouth volare"
|
||||||
|
19.4 6 232.0 90.00 3210. 17.2 78 1 "amc concord"
|
||||||
|
20.6 6 231.0 105.0 3380. 15.8 78 1 "buick century special"
|
||||||
|
20.8 6 200.0 85.00 3070. 16.7 78 1 "mercury zephyr"
|
||||||
|
18.6 6 225.0 110.0 3620. 18.7 78 1 "dodge aspen"
|
||||||
|
18.1 6 258.0 120.0 3410. 15.1 78 1 "amc concord d/l"
|
||||||
|
19.2 8 305.0 145.0 3425. 13.2 78 1 "chevrolet monte carlo landau"
|
||||||
|
17.7 6 231.0 165.0 3445. 13.4 78 1 "buick regal sport coupe (turbo)"
|
||||||
|
18.1 8 302.0 139.0 3205. 11.2 78 1 "ford futura"
|
||||||
|
17.5 8 318.0 140.0 4080. 13.7 78 1 "dodge magnum xe"
|
||||||
|
30.0 4 98.00 68.00 2155. 16.5 78 1 "chevrolet chevette"
|
||||||
|
27.5 4 134.0 95.00 2560. 14.2 78 3 "toyota corona"
|
||||||
|
27.2 4 119.0 97.00 2300. 14.7 78 3 "datsun 510"
|
||||||
|
30.9 4 105.0 75.00 2230. 14.5 78 1 "dodge omni"
|
||||||
|
21.1 4 134.0 95.00 2515. 14.8 78 3 "toyota celica gt liftback"
|
||||||
|
23.2 4 156.0 105.0 2745. 16.7 78 1 "plymouth sapporo"
|
||||||
|
23.8 4 151.0 85.00 2855. 17.6 78 1 "oldsmobile starfire sx"
|
||||||
|
23.9 4 119.0 97.00 2405. 14.9 78 3 "datsun 200-sx"
|
||||||
|
20.3 5 131.0 103.0 2830. 15.9 78 2 "audi 5000"
|
||||||
|
17.0 6 163.0 125.0 3140. 13.6 78 2 "volvo 264gl"
|
||||||
|
21.6 4 121.0 115.0 2795. 15.7 78 2 "saab 99gle"
|
||||||
|
16.2 6 163.0 133.0 3410. 15.8 78 2 "peugeot 604sl"
|
||||||
|
31.5 4 89.00 71.00 1990. 14.9 78 2 "volkswagen scirocco"
|
||||||
|
29.5 4 98.00 68.00 2135. 16.6 78 3 "honda accord lx"
|
||||||
|
21.5 6 231.0 115.0 3245. 15.4 79 1 "pontiac lemans v6"
|
||||||
|
19.8 6 200.0 85.00 2990. 18.2 79 1 "mercury zephyr 6"
|
||||||
|
22.3 4 140.0 88.00 2890. 17.3 79 1 "ford fairmont 4"
|
||||||
|
20.2 6 232.0 90.00 3265. 18.2 79 1 "amc concord dl 6"
|
||||||
|
20.6 6 225.0 110.0 3360. 16.6 79 1 "dodge aspen 6"
|
||||||
|
17.0 8 305.0 130.0 3840. 15.4 79 1 "chevrolet caprice classic"
|
||||||
|
17.6 8 302.0 129.0 3725. 13.4 79 1 "ford ltd landau"
|
||||||
|
16.5 8 351.0 138.0 3955. 13.2 79 1 "mercury grand marquis"
|
||||||
|
18.2 8 318.0 135.0 3830. 15.2 79 1 "dodge st. regis"
|
||||||
|
16.9 8 350.0 155.0 4360. 14.9 79 1 "buick estate wagon (sw)"
|
||||||
|
15.5 8 351.0 142.0 4054. 14.3 79 1 "ford country squire (sw)"
|
||||||
|
19.2 8 267.0 125.0 3605. 15.0 79 1 "chevrolet malibu classic (sw)"
|
||||||
|
18.5 8 360.0 150.0 3940. 13.0 79 1 "chrysler lebaron town @ country (sw)"
|
||||||
|
31.9 4 89.00 71.00 1925. 14.0 79 2 "vw rabbit custom"
|
||||||
|
34.1 4 86.00 65.00 1975. 15.2 79 3 "maxda glc deluxe"
|
||||||
|
35.7 4 98.00 80.00 1915. 14.4 79 1 "dodge colt hatchback custom"
|
||||||
|
27.4 4 121.0 80.00 2670. 15.0 79 1 "amc spirit dl"
|
||||||
|
25.4 5 183.0 77.00 3530. 20.1 79 2 "mercedes benz 300d"
|
||||||
|
23.0 8 350.0 125.0 3900. 17.4 79 1 "cadillac eldorado"
|
||||||
|
27.2 4 141.0 71.00 3190. 24.8 79 2 "peugeot 504"
|
||||||
|
23.9 8 260.0 90.00 3420. 22.2 79 1 "oldsmobile cutlass salon brougham"
|
||||||
|
34.2 4 105.0 70.00 2200. 13.2 79 1 "plymouth horizon"
|
||||||
|
34.5 4 105.0 70.00 2150. 14.9 79 1 "plymouth horizon tc3"
|
||||||
|
31.8 4 85.00 65.00 2020. 19.2 79 3 "datsun 210"
|
||||||
|
37.3 4 91.00 69.00 2130. 14.7 79 2 "fiat strada custom"
|
||||||
|
28.4 4 151.0 90.00 2670. 16.0 79 1 "buick skylark limited"
|
||||||
|
28.8 6 173.0 115.0 2595. 11.3 79 1 "chevrolet citation"
|
||||||
|
26.8 6 173.0 115.0 2700. 12.9 79 1 "oldsmobile omega brougham"
|
||||||
|
33.5 4 151.0 90.00 2556. 13.2 79 1 "pontiac phoenix"
|
||||||
|
41.5 4 98.00 76.00 2144. 14.7 80 2 "vw rabbit"
|
||||||
|
38.1 4 89.00 60.00 1968. 18.8 80 3 "toyota corolla tercel"
|
||||||
|
32.1 4 98.00 70.00 2120. 15.5 80 1 "chevrolet chevette"
|
||||||
|
37.2 4 86.00 65.00 2019. 16.4 80 3 "datsun 310"
|
||||||
|
28.0 4 151.0 90.00 2678. 16.5 80 1 "chevrolet citation"
|
||||||
|
26.4 4 140.0 88.00 2870. 18.1 80 1 "ford fairmont"
|
||||||
|
24.3 4 151.0 90.00 3003. 20.1 80 1 "amc concord"
|
||||||
|
19.1 6 225.0 90.00 3381. 18.7 80 1 "dodge aspen"
|
||||||
|
34.3 4 97.00 78.00 2188. 15.8 80 2 "audi 4000"
|
||||||
|
29.8 4 134.0 90.00 2711. 15.5 80 3 "toyota corona liftback"
|
||||||
|
31.3 4 120.0 75.00 2542. 17.5 80 3 "mazda 626"
|
||||||
|
37.0 4 119.0 92.00 2434. 15.0 80 3 "datsun 510 hatchback"
|
||||||
|
32.2 4 108.0 75.00 2265. 15.2 80 3 "toyota corolla"
|
||||||
|
46.6 4 86.00 65.00 2110. 17.9 80 3 "mazda glc"
|
||||||
|
27.9 4 156.0 105.0 2800. 14.4 80 1 "dodge colt"
|
||||||
|
40.8 4 85.00 65.00 2110. 19.2 80 3 "datsun 210"
|
||||||
|
44.3 4 90.00 48.00 2085. 21.7 80 2 "vw rabbit c (diesel)"
|
||||||
|
43.4 4 90.00 48.00 2335. 23.7 80 2 "vw dasher (diesel)"
|
||||||
|
36.4 5 121.0 67.00 2950. 19.9 80 2 "audi 5000s (diesel)"
|
||||||
|
30.0 4 146.0 67.00 3250. 21.8 80 2 "mercedes-benz 240d"
|
||||||
|
44.6 4 91.00 67.00 1850. 13.8 80 3 "honda civic 1500 gl"
|
||||||
|
40.9 4 85.00 ? 1835. 17.3 80 2 "renault lecar deluxe"
|
||||||
|
33.8 4 97.00 67.00 2145. 18.0 80 3 "subaru dl"
|
||||||
|
29.8 4 89.00 62.00 1845. 15.3 80 2 "vokswagen rabbit"
|
||||||
|
32.7 6 168.0 132.0 2910. 11.4 80 3 "datsun 280-zx"
|
||||||
|
23.7 3 70.00 100.0 2420. 12.5 80 3 "mazda rx-7 gs"
|
||||||
|
35.0 4 122.0 88.00 2500. 15.1 80 2 "triumph tr7 coupe"
|
||||||
|
23.6 4 140.0 ? 2905. 14.3 80 1 "ford mustang cobra"
|
||||||
|
32.4 4 107.0 72.00 2290. 17.0 80 3 "honda accord"
|
||||||
|
27.2 4 135.0 84.00 2490. 15.7 81 1 "plymouth reliant"
|
||||||
|
26.6 4 151.0 84.00 2635. 16.4 81 1 "buick skylark"
|
||||||
|
25.8 4 156.0 92.00 2620. 14.4 81 1 "dodge aries wagon (sw)"
|
||||||
|
23.5 6 173.0 110.0 2725. 12.6 81 1 "chevrolet citation"
|
||||||
|
30.0 4 135.0 84.00 2385. 12.9 81 1 "plymouth reliant"
|
||||||
|
39.1 4 79.00 58.00 1755. 16.9 81 3 "toyota starlet"
|
||||||
|
39.0 4 86.00 64.00 1875. 16.4 81 1 "plymouth champ"
|
||||||
|
35.1 4 81.00 60.00 1760. 16.1 81 3 "honda civic 1300"
|
||||||
|
32.3 4 97.00 67.00 2065. 17.8 81 3 "subaru"
|
||||||
|
37.0 4 85.00 65.00 1975. 19.4 81 3 "datsun 210 mpg"
|
||||||
|
37.7 4 89.00 62.00 2050. 17.3 81 3 "toyota tercel"
|
||||||
|
34.1 4 91.00 68.00 1985. 16.0 81 3 "mazda glc 4"
|
||||||
|
34.7 4 105.0 63.00 2215. 14.9 81 1 "plymouth horizon 4"
|
||||||
|
34.4 4 98.00 65.00 2045. 16.2 81 1 "ford escort 4w"
|
||||||
|
29.9 4 98.00 65.00 2380. 20.7 81 1 "ford escort 2h"
|
||||||
|
33.0 4 105.0 74.00 2190. 14.2 81 2 "volkswagen jetta"
|
||||||
|
34.5 4 100.0 ? 2320. 15.8 81 2 "renault 18i"
|
||||||
|
33.7 4 107.0 75.00 2210. 14.4 81 3 "honda prelude"
|
||||||
|
32.4 4 108.0 75.00 2350. 16.8 81 3 "toyota corolla"
|
||||||
|
32.9 4 119.0 100.0 2615. 14.8 81 3 "datsun 200sx"
|
||||||
|
31.6 4 120.0 74.00 2635. 18.3 81 3 "mazda 626"
|
||||||
|
28.1 4 141.0 80.00 3230. 20.4 81 2 "peugeot 505s turbo diesel"
|
||||||
|
30.7 6 145.0 76.00 3160. 19.6 81 2 "volvo diesel"
|
||||||
|
25.4 6 168.0 116.0 2900. 12.6 81 3 "toyota cressida"
|
||||||
|
24.2 6 146.0 120.0 2930. 13.8 81 3 "datsun 810 maxima"
|
||||||
|
22.4 6 231.0 110.0 3415. 15.8 81 1 "buick century"
|
||||||
|
26.6 8 350.0 105.0 3725. 19.0 81 1 "oldsmobile cutlass ls"
|
||||||
|
20.2 6 200.0 88.00 3060. 17.1 81 1 "ford granada gl"
|
||||||
|
17.6 6 225.0 85.00 3465. 16.6 81 1 "chrysler lebaron salon"
|
||||||
|
28.0 4 112.0 88.00 2605. 19.6 82 1 "chevrolet cavalier"
|
||||||
|
27.0 4 112.0 88.00 2640. 18.6 82 1 "chevrolet cavalier wagon"
|
||||||
|
34.0 4 112.0 88.00 2395. 18.0 82 1 "chevrolet cavalier 2-door"
|
||||||
|
31.0 4 112.0 85.00 2575. 16.2 82 1 "pontiac j2000 se hatchback"
|
||||||
|
29.0 4 135.0 84.00 2525. 16.0 82 1 "dodge aries se"
|
||||||
|
27.0 4 151.0 90.00 2735. 18.0 82 1 "pontiac phoenix"
|
||||||
|
24.0 4 140.0 92.00 2865. 16.4 82 1 "ford fairmont futura"
|
||||||
|
36.0 4 105.0 74.00 1980. 15.3 82 2 "volkswagen rabbit l"
|
||||||
|
37.0 4 91.00 68.00 2025. 18.2 82 3 "mazda glc custom l"
|
||||||
|
31.0 4 91.00 68.00 1970. 17.6 82 3 "mazda glc custom"
|
||||||
|
38.0 4 105.0 63.00 2125. 14.7 82 1 "plymouth horizon miser"
|
||||||
|
36.0 4 98.00 70.00 2125. 17.3 82 1 "mercury lynx l"
|
||||||
|
36.0 4 120.0 88.00 2160. 14.5 82 3 "nissan stanza xe"
|
||||||
|
36.0 4 107.0 75.00 2205. 14.5 82 3 "honda accord"
|
||||||
|
34.0 4 108.0 70.00 2245 16.9 82 3 "toyota corolla"
|
||||||
|
38.0 4 91.00 67.00 1965. 15.0 82 3 "honda civic"
|
||||||
|
32.0 4 91.00 67.00 1965. 15.7 82 3 "honda civic (auto)"
|
||||||
|
38.0 4 91.00 67.00 1995. 16.2 82 3 "datsun 310 gx"
|
||||||
|
25.0 6 181.0 110.0 2945. 16.4 82 1 "buick century limited"
|
||||||
|
38.0 6 262.0 85.00 3015. 17.0 82 1 "oldsmobile cutlass ciera (diesel)"
|
||||||
|
26.0 4 156.0 92.00 2585. 14.5 82 1 "chrysler lebaron medallion"
|
||||||
|
22.0 6 232.0 112.0 2835 14.7 82 1 "ford granada l"
|
||||||
|
32.0 4 144.0 96.00 2665. 13.9 82 3 "toyota celica gt"
|
||||||
|
36.0 4 135.0 84.00 2370. 13.0 82 1 "dodge charger 2.2"
|
||||||
|
27.0 4 151.0 90.00 2950. 17.3 82 1 "chevrolet camaro"
|
||||||
|
27.0 4 140.0 86.00 2790. 15.6 82 1 "ford mustang gl"
|
||||||
|
44.0 4 97.00 52.00 2130. 24.6 82 2 "vw pickup"
|
||||||
|
32.0 4 135.0 84.00 2295. 11.6 82 1 "dodge rampage"
|
||||||
|
28.0 4 120.0 79.00 2625. 18.6 82 1 "ford ranger"
|
||||||
|
31.0 4 119.0 82.00 2720. 19.4 82 1 "chevy s-10"
|
||||||
507
datasets/Boston.csv
Normal file
507
datasets/Boston.csv
Normal file
@ -0,0 +1,507 @@
|
|||||||
|
"","crim","zn","indus","chas","nox","rm","age","dis","rad","tax","ptratio","black","lstat","medv"
|
||||||
|
"1",0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98,24
|
||||||
|
"2",0.02731,0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14,21.6
|
||||||
|
"3",0.02729,0,7.07,0,0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03,34.7
|
||||||
|
"4",0.03237,0,2.18,0,0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94,33.4
|
||||||
|
"5",0.06905,0,2.18,0,0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33,36.2
|
||||||
|
"6",0.02985,0,2.18,0,0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21,28.7
|
||||||
|
"7",0.08829,12.5,7.87,0,0.524,6.012,66.6,5.5605,5,311,15.2,395.6,12.43,22.9
|
||||||
|
"8",0.14455,12.5,7.87,0,0.524,6.172,96.1,5.9505,5,311,15.2,396.9,19.15,27.1
|
||||||
|
"9",0.21124,12.5,7.87,0,0.524,5.631,100,6.0821,5,311,15.2,386.63,29.93,16.5
|
||||||
|
"10",0.17004,12.5,7.87,0,0.524,6.004,85.9,6.5921,5,311,15.2,386.71,17.1,18.9
|
||||||
|
"11",0.22489,12.5,7.87,0,0.524,6.377,94.3,6.3467,5,311,15.2,392.52,20.45,15
|
||||||
|
"12",0.11747,12.5,7.87,0,0.524,6.009,82.9,6.2267,5,311,15.2,396.9,13.27,18.9
|
||||||
|
"13",0.09378,12.5,7.87,0,0.524,5.889,39,5.4509,5,311,15.2,390.5,15.71,21.7
|
||||||
|
"14",0.62976,0,8.14,0,0.538,5.949,61.8,4.7075,4,307,21,396.9,8.26,20.4
|
||||||
|
"15",0.63796,0,8.14,0,0.538,6.096,84.5,4.4619,4,307,21,380.02,10.26,18.2
|
||||||
|
"16",0.62739,0,8.14,0,0.538,5.834,56.5,4.4986,4,307,21,395.62,8.47,19.9
|
||||||
|
"17",1.05393,0,8.14,0,0.538,5.935,29.3,4.4986,4,307,21,386.85,6.58,23.1
|
||||||
|
"18",0.7842,0,8.14,0,0.538,5.99,81.7,4.2579,4,307,21,386.75,14.67,17.5
|
||||||
|
"19",0.80271,0,8.14,0,0.538,5.456,36.6,3.7965,4,307,21,288.99,11.69,20.2
|
||||||
|
"20",0.7258,0,8.14,0,0.538,5.727,69.5,3.7965,4,307,21,390.95,11.28,18.2
|
||||||
|
"21",1.25179,0,8.14,0,0.538,5.57,98.1,3.7979,4,307,21,376.57,21.02,13.6
|
||||||
|
"22",0.85204,0,8.14,0,0.538,5.965,89.2,4.0123,4,307,21,392.53,13.83,19.6
|
||||||
|
"23",1.23247,0,8.14,0,0.538,6.142,91.7,3.9769,4,307,21,396.9,18.72,15.2
|
||||||
|
"24",0.98843,0,8.14,0,0.538,5.813,100,4.0952,4,307,21,394.54,19.88,14.5
|
||||||
|
"25",0.75026,0,8.14,0,0.538,5.924,94.1,4.3996,4,307,21,394.33,16.3,15.6
|
||||||
|
"26",0.84054,0,8.14,0,0.538,5.599,85.7,4.4546,4,307,21,303.42,16.51,13.9
|
||||||
|
"27",0.67191,0,8.14,0,0.538,5.813,90.3,4.682,4,307,21,376.88,14.81,16.6
|
||||||
|
"28",0.95577,0,8.14,0,0.538,6.047,88.8,4.4534,4,307,21,306.38,17.28,14.8
|
||||||
|
"29",0.77299,0,8.14,0,0.538,6.495,94.4,4.4547,4,307,21,387.94,12.8,18.4
|
||||||
|
"30",1.00245,0,8.14,0,0.538,6.674,87.3,4.239,4,307,21,380.23,11.98,21
|
||||||
|
"31",1.13081,0,8.14,0,0.538,5.713,94.1,4.233,4,307,21,360.17,22.6,12.7
|
||||||
|
"32",1.35472,0,8.14,0,0.538,6.072,100,4.175,4,307,21,376.73,13.04,14.5
|
||||||
|
"33",1.38799,0,8.14,0,0.538,5.95,82,3.99,4,307,21,232.6,27.71,13.2
|
||||||
|
"34",1.15172,0,8.14,0,0.538,5.701,95,3.7872,4,307,21,358.77,18.35,13.1
|
||||||
|
"35",1.61282,0,8.14,0,0.538,6.096,96.9,3.7598,4,307,21,248.31,20.34,13.5
|
||||||
|
"36",0.06417,0,5.96,0,0.499,5.933,68.2,3.3603,5,279,19.2,396.9,9.68,18.9
|
||||||
|
"37",0.09744,0,5.96,0,0.499,5.841,61.4,3.3779,5,279,19.2,377.56,11.41,20
|
||||||
|
"38",0.08014,0,5.96,0,0.499,5.85,41.5,3.9342,5,279,19.2,396.9,8.77,21
|
||||||
|
"39",0.17505,0,5.96,0,0.499,5.966,30.2,3.8473,5,279,19.2,393.43,10.13,24.7
|
||||||
|
"40",0.02763,75,2.95,0,0.428,6.595,21.8,5.4011,3,252,18.3,395.63,4.32,30.8
|
||||||
|
"41",0.03359,75,2.95,0,0.428,7.024,15.8,5.4011,3,252,18.3,395.62,1.98,34.9
|
||||||
|
"42",0.12744,0,6.91,0,0.448,6.77,2.9,5.7209,3,233,17.9,385.41,4.84,26.6
|
||||||
|
"43",0.1415,0,6.91,0,0.448,6.169,6.6,5.7209,3,233,17.9,383.37,5.81,25.3
|
||||||
|
"44",0.15936,0,6.91,0,0.448,6.211,6.5,5.7209,3,233,17.9,394.46,7.44,24.7
|
||||||
|
"45",0.12269,0,6.91,0,0.448,6.069,40,5.7209,3,233,17.9,389.39,9.55,21.2
|
||||||
|
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"301",0.04417,70,2.24,0,0.4,6.871,47.4,7.8278,5,358,14.8,390.86,6.07,24.8
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||||||
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||||||
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"308",0.04932,33,2.18,0,0.472,6.849,70.3,3.1827,7,222,18.4,396.9,7.53,28.2
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||||||
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"318",0.24522,0,9.9,0,0.544,5.782,71.7,4.0317,4,304,18.4,396.9,15.94,19.8
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"321",0.1676,0,7.38,0,0.493,6.426,52.3,4.5404,5,287,19.6,396.9,7.2,23.8
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"324",0.28392,0,7.38,0,0.493,5.708,74.3,4.7211,5,287,19.6,391.13,11.74,18.5
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"329",0.06617,0,3.24,0,0.46,5.868,25.8,5.2146,4,430,16.9,382.44,9.97,19.3
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"331",0.04544,0,3.24,0,0.46,6.144,32.2,5.8736,4,430,16.9,368.57,9.09,19.8
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"332",0.05023,35,6.06,0,0.4379,5.706,28.4,6.6407,1,304,16.9,394.02,12.43,17.1
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"334",0.05083,0,5.19,0,0.515,6.316,38.1,6.4584,5,224,20.2,389.71,5.68,22.2
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"335",0.03738,0,5.19,0,0.515,6.31,38.5,6.4584,5,224,20.2,389.4,6.75,20.7
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"336",0.03961,0,5.19,0,0.515,6.037,34.5,5.9853,5,224,20.2,396.9,8.01,21.1
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"338",0.03041,0,5.19,0,0.515,5.895,59.6,5.615,5,224,20.2,394.81,10.56,18.5
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"339",0.03306,0,5.19,0,0.515,6.059,37.3,4.8122,5,224,20.2,396.14,8.51,20.6
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"342",0.01301,35,1.52,0,0.442,7.241,49.3,7.0379,1,284,15.5,394.74,5.49,32.7
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"343",0.02498,0,1.89,0,0.518,6.54,59.7,6.2669,1,422,15.9,389.96,8.65,16.5
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||||||
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"344",0.02543,55,3.78,0,0.484,6.696,56.4,5.7321,5,370,17.6,396.9,7.18,23.9
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||||||
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"345",0.03049,55,3.78,0,0.484,6.874,28.1,6.4654,5,370,17.6,387.97,4.61,31.2
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||||||
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"346",0.03113,0,4.39,0,0.442,6.014,48.5,8.0136,3,352,18.8,385.64,10.53,17.5
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"347",0.06162,0,4.39,0,0.442,5.898,52.3,8.0136,3,352,18.8,364.61,12.67,17.2
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||||||
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"348",0.0187,85,4.15,0,0.429,6.516,27.7,8.5353,4,351,17.9,392.43,6.36,23.1
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|
"349",0.01501,80,2.01,0,0.435,6.635,29.7,8.344,4,280,17,390.94,5.99,24.5
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"350",0.02899,40,1.25,0,0.429,6.939,34.5,8.7921,1,335,19.7,389.85,5.89,26.6
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"351",0.06211,40,1.25,0,0.429,6.49,44.4,8.7921,1,335,19.7,396.9,5.98,22.9
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"352",0.0795,60,1.69,0,0.411,6.579,35.9,10.7103,4,411,18.3,370.78,5.49,24.1
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"354",0.01709,90,2.02,0,0.41,6.728,36.1,12.1265,5,187,17,384.46,4.5,30.1
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"355",0.04301,80,1.91,0,0.413,5.663,21.9,10.5857,4,334,22,382.8,8.05,18.2
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"356",0.10659,80,1.91,0,0.413,5.936,19.5,10.5857,4,334,22,376.04,5.57,20.6
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"359",5.20177,0,18.1,1,0.77,6.127,83.4,2.7227,24,666,20.2,395.43,11.48,22.7
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"366",4.55587,0,18.1,0,0.718,3.561,87.9,1.6132,24,666,20.2,354.7,7.12,27.5
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"367",3.69695,0,18.1,0,0.718,4.963,91.4,1.7523,24,666,20.2,316.03,14,21.9
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"368",13.5222,0,18.1,0,0.631,3.863,100,1.5106,24,666,20.2,131.42,13.33,23.1
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"372",9.2323,0,18.1,0,0.631,6.216,100,1.1691,24,666,20.2,366.15,9.53,50
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||||||
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"373",8.26725,0,18.1,1,0.668,5.875,89.6,1.1296,24,666,20.2,347.88,8.88,50
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||||||
|
"374",11.1081,0,18.1,0,0.668,4.906,100,1.1742,24,666,20.2,396.9,34.77,13.8
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||||||
|
"375",18.4982,0,18.1,0,0.668,4.138,100,1.137,24,666,20.2,396.9,37.97,13.8
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||||||
|
"376",19.6091,0,18.1,0,0.671,7.313,97.9,1.3163,24,666,20.2,396.9,13.44,15
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||||||
|
"377",15.288,0,18.1,0,0.671,6.649,93.3,1.3449,24,666,20.2,363.02,23.24,13.9
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||||||
|
"378",9.82349,0,18.1,0,0.671,6.794,98.8,1.358,24,666,20.2,396.9,21.24,13.3
|
||||||
|
"379",23.6482,0,18.1,0,0.671,6.38,96.2,1.3861,24,666,20.2,396.9,23.69,13.1
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||||||
|
"380",17.8667,0,18.1,0,0.671,6.223,100,1.3861,24,666,20.2,393.74,21.78,10.2
|
||||||
|
"381",88.9762,0,18.1,0,0.671,6.968,91.9,1.4165,24,666,20.2,396.9,17.21,10.4
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||||||
|
"382",15.8744,0,18.1,0,0.671,6.545,99.1,1.5192,24,666,20.2,396.9,21.08,10.9
|
||||||
|
"383",9.18702,0,18.1,0,0.7,5.536,100,1.5804,24,666,20.2,396.9,23.6,11.3
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||||||
|
"384",7.99248,0,18.1,0,0.7,5.52,100,1.5331,24,666,20.2,396.9,24.56,12.3
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||||||
|
"385",20.0849,0,18.1,0,0.7,4.368,91.2,1.4395,24,666,20.2,285.83,30.63,8.8
|
||||||
|
"386",16.8118,0,18.1,0,0.7,5.277,98.1,1.4261,24,666,20.2,396.9,30.81,7.2
|
||||||
|
"387",24.3938,0,18.1,0,0.7,4.652,100,1.4672,24,666,20.2,396.9,28.28,10.5
|
||||||
|
"388",22.5971,0,18.1,0,0.7,5,89.5,1.5184,24,666,20.2,396.9,31.99,7.4
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||||||
|
"389",14.3337,0,18.1,0,0.7,4.88,100,1.5895,24,666,20.2,372.92,30.62,10.2
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||||||
|
"390",8.15174,0,18.1,0,0.7,5.39,98.9,1.7281,24,666,20.2,396.9,20.85,11.5
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||||||
|
"391",6.96215,0,18.1,0,0.7,5.713,97,1.9265,24,666,20.2,394.43,17.11,15.1
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||||||
|
"392",5.29305,0,18.1,0,0.7,6.051,82.5,2.1678,24,666,20.2,378.38,18.76,23.2
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||||||
|
"393",11.5779,0,18.1,0,0.7,5.036,97,1.77,24,666,20.2,396.9,25.68,9.7
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||||||
|
"394",8.64476,0,18.1,0,0.693,6.193,92.6,1.7912,24,666,20.2,396.9,15.17,13.8
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||||||
|
"395",13.3598,0,18.1,0,0.693,5.887,94.7,1.7821,24,666,20.2,396.9,16.35,12.7
|
||||||
|
"396",8.71675,0,18.1,0,0.693,6.471,98.8,1.7257,24,666,20.2,391.98,17.12,13.1
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||||||
|
"397",5.87205,0,18.1,0,0.693,6.405,96,1.6768,24,666,20.2,396.9,19.37,12.5
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||||||
|
"398",7.67202,0,18.1,0,0.693,5.747,98.9,1.6334,24,666,20.2,393.1,19.92,8.5
|
||||||
|
"399",38.3518,0,18.1,0,0.693,5.453,100,1.4896,24,666,20.2,396.9,30.59,5
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||||||
|
"400",9.91655,0,18.1,0,0.693,5.852,77.8,1.5004,24,666,20.2,338.16,29.97,6.3
|
||||||
|
"401",25.0461,0,18.1,0,0.693,5.987,100,1.5888,24,666,20.2,396.9,26.77,5.6
|
||||||
|
"402",14.2362,0,18.1,0,0.693,6.343,100,1.5741,24,666,20.2,396.9,20.32,7.2
|
||||||
|
"403",9.59571,0,18.1,0,0.693,6.404,100,1.639,24,666,20.2,376.11,20.31,12.1
|
||||||
|
"404",24.8017,0,18.1,0,0.693,5.349,96,1.7028,24,666,20.2,396.9,19.77,8.3
|
||||||
|
"405",41.5292,0,18.1,0,0.693,5.531,85.4,1.6074,24,666,20.2,329.46,27.38,8.5
|
||||||
|
"406",67.9208,0,18.1,0,0.693,5.683,100,1.4254,24,666,20.2,384.97,22.98,5
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||||||
|
"407",20.7162,0,18.1,0,0.659,4.138,100,1.1781,24,666,20.2,370.22,23.34,11.9
|
||||||
|
"408",11.9511,0,18.1,0,0.659,5.608,100,1.2852,24,666,20.2,332.09,12.13,27.9
|
||||||
|
"409",7.40389,0,18.1,0,0.597,5.617,97.9,1.4547,24,666,20.2,314.64,26.4,17.2
|
||||||
|
"410",14.4383,0,18.1,0,0.597,6.852,100,1.4655,24,666,20.2,179.36,19.78,27.5
|
||||||
|
"411",51.1358,0,18.1,0,0.597,5.757,100,1.413,24,666,20.2,2.6,10.11,15
|
||||||
|
"412",14.0507,0,18.1,0,0.597,6.657,100,1.5275,24,666,20.2,35.05,21.22,17.2
|
||||||
|
"413",18.811,0,18.1,0,0.597,4.628,100,1.5539,24,666,20.2,28.79,34.37,17.9
|
||||||
|
"414",28.6558,0,18.1,0,0.597,5.155,100,1.5894,24,666,20.2,210.97,20.08,16.3
|
||||||
|
"415",45.7461,0,18.1,0,0.693,4.519,100,1.6582,24,666,20.2,88.27,36.98,7
|
||||||
|
"416",18.0846,0,18.1,0,0.679,6.434,100,1.8347,24,666,20.2,27.25,29.05,7.2
|
||||||
|
"417",10.8342,0,18.1,0,0.679,6.782,90.8,1.8195,24,666,20.2,21.57,25.79,7.5
|
||||||
|
"418",25.9406,0,18.1,0,0.679,5.304,89.1,1.6475,24,666,20.2,127.36,26.64,10.4
|
||||||
|
"419",73.5341,0,18.1,0,0.679,5.957,100,1.8026,24,666,20.2,16.45,20.62,8.8
|
||||||
|
"420",11.8123,0,18.1,0,0.718,6.824,76.5,1.794,24,666,20.2,48.45,22.74,8.4
|
||||||
|
"421",11.0874,0,18.1,0,0.718,6.411,100,1.8589,24,666,20.2,318.75,15.02,16.7
|
||||||
|
"422",7.02259,0,18.1,0,0.718,6.006,95.3,1.8746,24,666,20.2,319.98,15.7,14.2
|
||||||
|
"423",12.0482,0,18.1,0,0.614,5.648,87.6,1.9512,24,666,20.2,291.55,14.1,20.8
|
||||||
|
"424",7.05042,0,18.1,0,0.614,6.103,85.1,2.0218,24,666,20.2,2.52,23.29,13.4
|
||||||
|
"425",8.79212,0,18.1,0,0.584,5.565,70.6,2.0635,24,666,20.2,3.65,17.16,11.7
|
||||||
|
"426",15.8603,0,18.1,0,0.679,5.896,95.4,1.9096,24,666,20.2,7.68,24.39,8.3
|
||||||
|
"427",12.2472,0,18.1,0,0.584,5.837,59.7,1.9976,24,666,20.2,24.65,15.69,10.2
|
||||||
|
"428",37.6619,0,18.1,0,0.679,6.202,78.7,1.8629,24,666,20.2,18.82,14.52,10.9
|
||||||
|
"429",7.36711,0,18.1,0,0.679,6.193,78.1,1.9356,24,666,20.2,96.73,21.52,11
|
||||||
|
"430",9.33889,0,18.1,0,0.679,6.38,95.6,1.9682,24,666,20.2,60.72,24.08,9.5
|
||||||
|
"431",8.49213,0,18.1,0,0.584,6.348,86.1,2.0527,24,666,20.2,83.45,17.64,14.5
|
||||||
|
"432",10.0623,0,18.1,0,0.584,6.833,94.3,2.0882,24,666,20.2,81.33,19.69,14.1
|
||||||
|
"433",6.44405,0,18.1,0,0.584,6.425,74.8,2.2004,24,666,20.2,97.95,12.03,16.1
|
||||||
|
"434",5.58107,0,18.1,0,0.713,6.436,87.9,2.3158,24,666,20.2,100.19,16.22,14.3
|
||||||
|
"435",13.9134,0,18.1,0,0.713,6.208,95,2.2222,24,666,20.2,100.63,15.17,11.7
|
||||||
|
"436",11.1604,0,18.1,0,0.74,6.629,94.6,2.1247,24,666,20.2,109.85,23.27,13.4
|
||||||
|
"437",14.4208,0,18.1,0,0.74,6.461,93.3,2.0026,24,666,20.2,27.49,18.05,9.6
|
||||||
|
"438",15.1772,0,18.1,0,0.74,6.152,100,1.9142,24,666,20.2,9.32,26.45,8.7
|
||||||
|
"439",13.6781,0,18.1,0,0.74,5.935,87.9,1.8206,24,666,20.2,68.95,34.02,8.4
|
||||||
|
"440",9.39063,0,18.1,0,0.74,5.627,93.9,1.8172,24,666,20.2,396.9,22.88,12.8
|
||||||
|
"441",22.0511,0,18.1,0,0.74,5.818,92.4,1.8662,24,666,20.2,391.45,22.11,10.5
|
||||||
|
"442",9.72418,0,18.1,0,0.74,6.406,97.2,2.0651,24,666,20.2,385.96,19.52,17.1
|
||||||
|
"443",5.66637,0,18.1,0,0.74,6.219,100,2.0048,24,666,20.2,395.69,16.59,18.4
|
||||||
|
"444",9.96654,0,18.1,0,0.74,6.485,100,1.9784,24,666,20.2,386.73,18.85,15.4
|
||||||
|
"445",12.8023,0,18.1,0,0.74,5.854,96.6,1.8956,24,666,20.2,240.52,23.79,10.8
|
||||||
|
"446",10.6718,0,18.1,0,0.74,6.459,94.8,1.9879,24,666,20.2,43.06,23.98,11.8
|
||||||
|
"447",6.28807,0,18.1,0,0.74,6.341,96.4,2.072,24,666,20.2,318.01,17.79,14.9
|
||||||
|
"448",9.92485,0,18.1,0,0.74,6.251,96.6,2.198,24,666,20.2,388.52,16.44,12.6
|
||||||
|
"449",9.32909,0,18.1,0,0.713,6.185,98.7,2.2616,24,666,20.2,396.9,18.13,14.1
|
||||||
|
"450",7.52601,0,18.1,0,0.713,6.417,98.3,2.185,24,666,20.2,304.21,19.31,13
|
||||||
|
"451",6.71772,0,18.1,0,0.713,6.749,92.6,2.3236,24,666,20.2,0.32,17.44,13.4
|
||||||
|
"452",5.44114,0,18.1,0,0.713,6.655,98.2,2.3552,24,666,20.2,355.29,17.73,15.2
|
||||||
|
"453",5.09017,0,18.1,0,0.713,6.297,91.8,2.3682,24,666,20.2,385.09,17.27,16.1
|
||||||
|
"454",8.24809,0,18.1,0,0.713,7.393,99.3,2.4527,24,666,20.2,375.87,16.74,17.8
|
||||||
|
"455",9.51363,0,18.1,0,0.713,6.728,94.1,2.4961,24,666,20.2,6.68,18.71,14.9
|
||||||
|
"456",4.75237,0,18.1,0,0.713,6.525,86.5,2.4358,24,666,20.2,50.92,18.13,14.1
|
||||||
|
"457",4.66883,0,18.1,0,0.713,5.976,87.9,2.5806,24,666,20.2,10.48,19.01,12.7
|
||||||
|
"458",8.20058,0,18.1,0,0.713,5.936,80.3,2.7792,24,666,20.2,3.5,16.94,13.5
|
||||||
|
"459",7.75223,0,18.1,0,0.713,6.301,83.7,2.7831,24,666,20.2,272.21,16.23,14.9
|
||||||
|
"460",6.80117,0,18.1,0,0.713,6.081,84.4,2.7175,24,666,20.2,396.9,14.7,20
|
||||||
|
"461",4.81213,0,18.1,0,0.713,6.701,90,2.5975,24,666,20.2,255.23,16.42,16.4
|
||||||
|
"462",3.69311,0,18.1,0,0.713,6.376,88.4,2.5671,24,666,20.2,391.43,14.65,17.7
|
||||||
|
"463",6.65492,0,18.1,0,0.713,6.317,83,2.7344,24,666,20.2,396.9,13.99,19.5
|
||||||
|
"464",5.82115,0,18.1,0,0.713,6.513,89.9,2.8016,24,666,20.2,393.82,10.29,20.2
|
||||||
|
"465",7.83932,0,18.1,0,0.655,6.209,65.4,2.9634,24,666,20.2,396.9,13.22,21.4
|
||||||
|
"466",3.1636,0,18.1,0,0.655,5.759,48.2,3.0665,24,666,20.2,334.4,14.13,19.9
|
||||||
|
"467",3.77498,0,18.1,0,0.655,5.952,84.7,2.8715,24,666,20.2,22.01,17.15,19
|
||||||
|
"468",4.42228,0,18.1,0,0.584,6.003,94.5,2.5403,24,666,20.2,331.29,21.32,19.1
|
||||||
|
"469",15.5757,0,18.1,0,0.58,5.926,71,2.9084,24,666,20.2,368.74,18.13,19.1
|
||||||
|
"470",13.0751,0,18.1,0,0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76,20.1
|
||||||
|
"471",4.34879,0,18.1,0,0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29,19.9
|
||||||
|
"472",4.03841,0,18.1,0,0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87,19.6
|
||||||
|
"473",3.56868,0,18.1,0,0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36,23.2
|
||||||
|
"474",4.64689,0,18.1,0,0.614,6.98,67.6,2.5329,24,666,20.2,374.68,11.66,29.8
|
||||||
|
"475",8.05579,0,18.1,0,0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14,13.8
|
||||||
|
"476",6.39312,0,18.1,0,0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1,13.3
|
||||||
|
"477",4.87141,0,18.1,0,0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68,16.7
|
||||||
|
"478",15.0234,0,18.1,0,0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91,12
|
||||||
|
"479",10.233,0,18.1,0,0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03,14.6
|
||||||
|
"480",14.3337,0,18.1,0,0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11,21.4
|
||||||
|
"481",5.82401,0,18.1,0,0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74,23
|
||||||
|
"482",5.70818,0,18.1,0,0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74,23.7
|
||||||
|
"483",5.73116,0,18.1,0,0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01,25
|
||||||
|
"484",2.81838,0,18.1,0,0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42,21.8
|
||||||
|
"485",2.37857,0,18.1,0,0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34,20.6
|
||||||
|
"486",3.67367,0,18.1,0,0.583,6.312,51.9,3.9917,24,666,20.2,388.62,10.58,21.2
|
||||||
|
"487",5.69175,0,18.1,0,0.583,6.114,79.8,3.5459,24,666,20.2,392.68,14.98,19.1
|
||||||
|
"488",4.83567,0,18.1,0,0.583,5.905,53.2,3.1523,24,666,20.2,388.22,11.45,20.6
|
||||||
|
"489",0.15086,0,27.74,0,0.609,5.454,92.7,1.8209,4,711,20.1,395.09,18.06,15.2
|
||||||
|
"490",0.18337,0,27.74,0,0.609,5.414,98.3,1.7554,4,711,20.1,344.05,23.97,7
|
||||||
|
"491",0.20746,0,27.74,0,0.609,5.093,98,1.8226,4,711,20.1,318.43,29.68,8.1
|
||||||
|
"492",0.10574,0,27.74,0,0.609,5.983,98.8,1.8681,4,711,20.1,390.11,18.07,13.6
|
||||||
|
"493",0.11132,0,27.74,0,0.609,5.983,83.5,2.1099,4,711,20.1,396.9,13.35,20.1
|
||||||
|
"494",0.17331,0,9.69,0,0.585,5.707,54,2.3817,6,391,19.2,396.9,12.01,21.8
|
||||||
|
"495",0.27957,0,9.69,0,0.585,5.926,42.6,2.3817,6,391,19.2,396.9,13.59,24.5
|
||||||
|
"496",0.17899,0,9.69,0,0.585,5.67,28.8,2.7986,6,391,19.2,393.29,17.6,23.1
|
||||||
|
"497",0.2896,0,9.69,0,0.585,5.39,72.9,2.7986,6,391,19.2,396.9,21.14,19.7
|
||||||
|
"498",0.26838,0,9.69,0,0.585,5.794,70.6,2.8927,6,391,19.2,396.9,14.1,18.3
|
||||||
|
"499",0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92,21.2
|
||||||
|
"500",0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1,17.5
|
||||||
|
"501",0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33,16.8
|
||||||
|
"502",0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,391.99,9.67,22.4
|
||||||
|
"503",0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08,20.6
|
||||||
|
"504",0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64,23.9
|
||||||
|
"505",0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48,22
|
||||||
|
"506",0.04741,0,11.93,0,0.573,6.03,80.8,2.505,1,273,21,396.9,7.88,11.9
|
||||||
|
1000
datasets/Ch10Ex11.csv
Normal file
1000
datasets/Ch10Ex11.csv
Normal file
File diff suppressed because it is too large
Load Diff
778
datasets/College.csv
Normal file
778
datasets/College.csv
Normal file
@ -0,0 +1,778 @@
|
|||||||
|
,Private,Apps,Accept,Enroll,Top10perc,Top25perc,F.Undergrad,P.Undergrad,Outstate,Room.Board,Books,Personal,PhD,Terminal,S.F.Ratio,perc.alumni,Expend,Grad.Rate
|
||||||
|
Abilene Christian University,Yes,1660,1232,721,23,52,2885,537,7440,3300,450,2200,70,78,18.1,12,7041,60
|
||||||
|
Adelphi University,Yes,2186,1924,512,16,29,2683,1227,12280,6450,750,1500,29,30,12.2,16,10527,56
|
||||||
|
Adrian College,Yes,1428,1097,336,22,50,1036,99,11250,3750,400,1165,53,66,12.9,30,8735,54
|
||||||
|
Agnes Scott College,Yes,417,349,137,60,89,510,63,12960,5450,450,875,92,97,7.7,37,19016,59
|
||||||
|
Alaska Pacific University,Yes,193,146,55,16,44,249,869,7560,4120,800,1500,76,72,11.9,2,10922,15
|
||||||
|
Albertson College,Yes,587,479,158,38,62,678,41,13500,3335,500,675,67,73,9.4,11,9727,55
|
||||||
|
Albertus Magnus College,Yes,353,340,103,17,45,416,230,13290,5720,500,1500,90,93,11.5,26,8861,63
|
||||||
|
Albion College,Yes,1899,1720,489,37,68,1594,32,13868,4826,450,850,89,100,13.7,37,11487,73
|
||||||
|
Albright College,Yes,1038,839,227,30,63,973,306,15595,4400,300,500,79,84,11.3,23,11644,80
|
||||||
|
Alderson-Broaddus College,Yes,582,498,172,21,44,799,78,10468,3380,660,1800,40,41,11.5,15,8991,52
|
||||||
|
Alfred University,Yes,1732,1425,472,37,75,1830,110,16548,5406,500,600,82,88,11.3,31,10932,73
|
||||||
|
Allegheny College,Yes,2652,1900,484,44,77,1707,44,17080,4440,400,600,73,91,9.9,41,11711,76
|
||||||
|
Allentown Coll. of St. Francis de Sales,Yes,1179,780,290,38,64,1130,638,9690,4785,600,1000,60,84,13.3,21,7940,74
|
||||||
|
Alma College,Yes,1267,1080,385,44,73,1306,28,12572,4552,400,400,79,87,15.3,32,9305,68
|
||||||
|
Alverno College,Yes,494,313,157,23,46,1317,1235,8352,3640,650,2449,36,69,11.1,26,8127,55
|
||||||
|
American International College,Yes,1420,1093,220,9,22,1018,287,8700,4780,450,1400,78,84,14.7,19,7355,69
|
||||||
|
Amherst College,Yes,4302,992,418,83,96,1593,5,19760,5300,660,1598,93,98,8.4,63,21424,100
|
||||||
|
Anderson University,Yes,1216,908,423,19,40,1819,281,10100,3520,550,1100,48,61,12.1,14,7994,59
|
||||||
|
Andrews University,Yes,1130,704,322,14,23,1586,326,9996,3090,900,1320,62,66,11.5,18,10908,46
|
||||||
|
Angelo State University,No,3540,2001,1016,24,54,4190,1512,5130,3592,500,2000,60,62,23.1,5,4010,34
|
||||||
|
Antioch University,Yes,713,661,252,25,44,712,23,15476,3336,400,1100,69,82,11.3,35,42926,48
|
||||||
|
Appalachian State University,No,7313,4664,1910,20,63,9940,1035,6806,2540,96,2000,83,96,18.3,14,5854,70
|
||||||
|
Aquinas College,Yes,619,516,219,20,51,1251,767,11208,4124,350,1615,55,65,12.7,25,6584,65
|
||||||
|
Arizona State University Main campus,No,12809,10308,3761,24,49,22593,7585,7434,4850,700,2100,88,93,18.9,5,4602,48
|
||||||
|
Arkansas College (Lyon College),Yes,708,334,166,46,74,530,182,8644,3922,500,800,79,88,12.6,24,14579,54
|
||||||
|
Arkansas Tech University,No,1734,1729,951,12,52,3602,939,3460,2650,450,1000,57,60,19.6,5,4739,48
|
||||||
|
Assumption College,Yes,2135,1700,491,23,59,1708,689,12000,5920,500,500,93,93,13.8,30,7100,88
|
||||||
|
Auburn University-Main Campus,No,7548,6791,3070,25,57,16262,1716,6300,3933,600,1908,85,91,16.7,18,6642,69
|
||||||
|
Augsburg College,Yes,662,513,257,12,30,2074,726,11902,4372,540,950,65,65,12.8,31,7836,58
|
||||||
|
Augustana College IL,Yes,1879,1658,497,36,69,1950,38,13353,4173,540,821,78,83,12.7,40,9220,71
|
||||||
|
Augustana College,Yes,761,725,306,21,58,1337,300,10990,3244,600,1021,66,70,10.4,30,6871,69
|
||||||
|
Austin College,Yes,948,798,295,42,74,1120,15,11280,4342,400,1150,81,95,13,33,11361,71
|
||||||
|
Averett College,Yes,627,556,172,16,40,777,538,9925,4135,750,1350,59,67,22.4,11,6523,48
|
||||||
|
Baker University,Yes,602,483,206,21,47,958,466,8620,4100,400,2250,58,68,11,21,6136,65
|
||||||
|
Baldwin-Wallace College,Yes,1690,1366,662,30,61,2718,1460,10995,4410,1000,1000,68,74,17.6,20,8086,85
|
||||||
|
Barat College,Yes,261,192,111,15,36,453,266,9690,4300,500,500,57,77,9.7,35,9337,71
|
||||||
|
Bard College,Yes,1910,838,285,50,85,1004,15,19264,6206,750,750,98,98,10.4,30,13894,79
|
||||||
|
Barnard College,Yes,2496,1402,531,53,95,2121,69,17926,8124,600,850,83,93,10.3,33,12580,91
|
||||||
|
Barry University,Yes,990,784,279,18,45,1811,3144,11290,5360,600,1800,76,78,12.6,11,9084,72
|
||||||
|
Baylor University,Yes,6075,5349,2367,34,66,9919,484,6450,3920,600,1346,71,76,18.5,38,7503,72
|
||||||
|
Beaver College,Yes,1163,850,348,23,56,878,519,12850,5400,400,800,78,89,12.2,30,8954,73
|
||||||
|
Bellarmine College,Yes,807,707,308,39,63,1198,605,8840,2950,750,1290,74,82,13.1,31,6668,84
|
||||||
|
Belmont Abbey College,Yes,632,494,129,17,36,709,131,9000,4850,300,2480,78,85,13.2,10,7550,52
|
||||||
|
Belmont University,Yes,1220,974,481,28,67,1964,623,7800,3664,650,900,61,61,11.1,19,7614,49
|
||||||
|
Beloit College,Yes,1320,923,284,26,54,1085,81,16304,3616,355,715,87,95,11.1,26,12957,69
|
||||||
|
Bemidji State University,No,1208,877,546,12,36,3796,824,4425,2700,660,1800,57,62,19.6,16,3752,46
|
||||||
|
Benedictine College,Yes,632,620,222,14,24,702,501,9550,3850,350,250,64,84,14.1,18,5922,58
|
||||||
|
Bennington College,Yes,519,327,114,25,53,457,2,21700,4100,600,500,35,59,10.1,33,16364,55
|
||||||
|
Bentley College,Yes,3466,2330,640,20,60,3095,1533,13800,5510,630,850,87,87,17.5,20,10941,82
|
||||||
|
Berry College,Yes,1858,1221,480,37,68,1620,49,8050,3940,350,2375,80,80,16.3,17,10511,63
|
||||||
|
Bethany College,Yes,878,816,200,16,41,706,62,8740,3363,550,1700,62,68,11.6,29,7718,48
|
||||||
|
Bethel College KS,Yes,202,184,122,19,42,537,101,8540,3580,500,1400,61,80,8.8,32,8324,56
|
||||||
|
Bethel College,Yes,502,384,104,11,28,347,74,6200,2900,600,800,63,63,11.7,13,7623,35
|
||||||
|
Bethune Cookman College,Yes,1646,1150,542,12,30,2128,82,5188,3396,650,2500,48,48,13.8,9,6817,58
|
||||||
|
Birmingham-Southern College,Yes,805,588,287,67,88,1376,207,11660,4325,400,900,74,79,14,34,8649,72
|
||||||
|
Blackburn College,Yes,500,336,156,25,55,421,27,6500,2700,500,1000,76,76,14.3,53,8377,51
|
||||||
|
Bloomsburg Univ. of Pennsylvania,No,6773,3028,1025,15,55,5847,946,7844,2948,500,1680,66,68,18,19,7041,75
|
||||||
|
Bluefield College,Yes,377,358,181,15,30,653,129,7150,4350,450,1500,61,67,17.8,3,6259,53
|
||||||
|
Bluffton College,Yes,692,514,209,20,50,760,81,9900,3990,400,900,76,71,13.3,19,9073,58
|
||||||
|
Boston University,Yes,20192,13007,3810,45,80,14971,3113,18420,6810,475,1025,80,81,11.9,16,16836,72
|
||||||
|
Bowdoin College,Yes,3356,1019,418,76,100,1490,8,19030,5885,1495,875,93,96,11.2,52,20447,96
|
||||||
|
Bowling Green State University,No,9251,7333,3076,14,45,13699,1213,7452,3352,600,1700,81,89,21.1,14,6918,67
|
||||||
|
Bradford College,Yes,443,330,151,5,36,453,42,14080,6270,500,900,57,80,10.2,21,15387,46
|
||||||
|
Bradley University,Yes,3767,3414,1061,30,58,4531,643,10870,4440,2000,1522,75,81,14.4,21,7671,85
|
||||||
|
Brandeis University,Yes,4186,2743,740,48,77,2819,62,19380,6750,410,1000,90,97,9.8,24,17150,84
|
||||||
|
Brenau University,Yes,367,274,158,12,41,917,479,9592,5879,500,700,71,80,13.7,12,5935,49
|
||||||
|
Brewton-Parker College,Yes,1436,1228,1202,10,26,1320,822,4371,2370,500,2000,62,62,12.6,10,4900,18
|
||||||
|
Briar Cliff College,Yes,392,351,155,16,44,738,430,10260,3597,600,1500,39,66,13.1,26,8355,58
|
||||||
|
Bridgewater College,Yes,838,673,292,22,53,881,55,10265,4725,560,875,68,73,13.2,24,8655,82
|
||||||
|
Brigham Young University at Provo,Yes,7365,5402,4615,48,82,27378,1253,2340,3580,860,1220,76,76,20.5,40,7916,33
|
||||||
|
Brown University,Yes,12586,3239,1462,87,95,5643,349,19528,5926,720,1100,99,100,7.6,39,20440,97
|
||||||
|
Bryn Mawr College,Yes,1465,810,313,71,95,1088,16,18165,6750,500,1200,100,100,12.3,49,17449,89
|
||||||
|
Bucknell University,Yes,6548,3813,862,49,85,3316,31,18550,4750,800,1200,95,97,14.2,36,13675,93
|
||||||
|
Buena Vista College,Yes,860,688,285,32,70,1928,442,13306,3797,450,950,62,69,8.8,10,6333,78
|
||||||
|
Butler University,Yes,2362,2037,700,40,68,2607,148,13130,4650,500,1600,77,81,10.9,29,9511,83
|
||||||
|
Cabrini College,Yes,599,494,224,8,28,1035,446,10518,6250,300,300,59,76,16.5,36,7117,71
|
||||||
|
Caldwell College,Yes,1011,604,213,17,42,693,868,8900,4600,425,1000,87,96,13.9,25,7922,55
|
||||||
|
California Lutheran University,Yes,563,247,247,23,52,1427,432,12950,5300,612,576,72,74,12.4,17,8985,60
|
||||||
|
California Polytechnic-San Luis,No,7811,3817,1650,47,73,12911,1404,7380,4877,612,2091,72,81,19.8,13,8453,59
|
||||||
|
California State University at Fresno,No,4540,3294,1483,5,60,13494,1254,7706,4368,600,1926,90,90,21.2,8,7268,61
|
||||||
|
Calvin College,Yes,1784,1512,913,29,56,3401,136,10230,3710,400,1210,75,81,14.8,41,7786,81
|
||||||
|
Campbell University,Yes,2087,1339,657,20,54,3191,1204,7550,2790,600,500,77,77,21.8,34,3739,63
|
||||||
|
Campbellsville College,Yes,848,587,298,25,55,935,184,6060,3070,600,1300,62,66,17.7,13,5391,49
|
||||||
|
Canisius College,Yes,2853,2193,753,16,34,2978,434,10750,5340,400,1130,90,92,14.6,26,7972,64
|
||||||
|
Capital University,Yes,1747,1382,449,34,66,1662,960,13050,4000,500,800,64,69,12.1,27,9557,83
|
||||||
|
Capitol College,Yes,100,90,35,10,52,282,331,8400,2812,300,2134,10,50,12.1,24,7976,52
|
||||||
|
Carleton College,Yes,2694,1579,489,75,93,1870,12,19292,3957,550,550,81,93,10.4,60,17960,91
|
||||||
|
Carnegie Mellon University,Yes,8728,5201,1191,60,89,4265,291,17900,5690,450,1250,86,93,9.2,31,24386,74
|
||||||
|
Carroll College,Yes,1160,991,352,19,55,1357,737,12200,3880,480,930,74,81,17.8,25,7666,79
|
||||||
|
Carson-Newman College,Yes,1096,951,464,27,62,1776,239,8150,3150,400,500,61,62,13.6,16,6716,67
|
||||||
|
Carthage College,Yes,1616,1427,434,20,43,1405,580,13125,3775,500,1300,74,89,15.9,22,7364,62
|
||||||
|
Case Western Reserve University,Yes,3877,3156,713,71,93,3051,513,15700,4730,525,1460,95,95,2.9,29,19733,67
|
||||||
|
Castleton State College,No,1257,940,363,9,22,1547,294,7656,4690,400,700,89,91,14.7,8,6318,79
|
||||||
|
Catawba College,Yes,1083,880,291,13,34,915,80,9270,4100,600,1860,75,82,13.5,27,8425,55
|
||||||
|
Catholic University of America,Yes,1754,1465,505,24,49,2159,211,13712,6408,526,1100,90,96,9.3,18,12751,75
|
||||||
|
Cazenovia College,Yes,3847,3433,527,9,35,1010,12,9384,4840,600,500,22,47,14.3,20,7697,118
|
||||||
|
Cedar Crest College,Yes,776,607,198,25,58,791,764,14340,5285,500,1000,58,83,11.7,39,10961,74
|
||||||
|
Cedarville College,Yes,1307,1090,616,25,55,2196,82,7344,4410,570,1000,50,52,15.3,34,6897,64
|
||||||
|
Centenary College,Yes,369,312,90,12,46,396,526,11400,5400,500,760,41,85,9.5,20,9583,24
|
||||||
|
Centenary College of Louisiana,Yes,495,434,210,35,55,775,44,8950,3490,600,1900,86,92,11.3,25,9685,66
|
||||||
|
Center for Creative Studies,Yes,601,396,203,1,20,525,323,11230,6643,2340,620,8,58,6.8,4,13025,47
|
||||||
|
Central College,Yes,1283,1113,401,31,65,1355,40,10938,3660,650,600,76,90,13.5,29,8444,67
|
||||||
|
Central Connecticut State University,No,4158,2532,902,6,24,6394,3881,5962,4444,500,985,69,73,16.7,4,4900,49
|
||||||
|
Central Missouri State University,No,4681,4101,1436,10,35,8094,1596,4620,3288,300,2250,69,80,19.7,4,5501,50
|
||||||
|
Central Washington University,No,2785,2011,1007,8,65,6507,898,7242,3603,654,1416,67,89,18.1,0,6413,51
|
||||||
|
Central Wesleyan College,Yes,174,146,88,8,29,1047,33,8300,3080,600,600,62,62,15.2,18,3365,58
|
||||||
|
Centre College,Yes,1013,888,288,55,82,943,7,11850,4270,600,900,95,99,11.4,60,13118,74
|
||||||
|
Chapman University,Yes,959,771,351,23,48,1662,209,16624,5895,600,1100,72,80,12.8,6,12692,47
|
||||||
|
Chatham College,Yes,212,197,91,28,56,471,148,13500,5230,400,850,95,98,9.3,37,16095,52
|
||||||
|
Chestnut Hill College,Yes,342,254,126,25,64,518,232,10335,5015,700,850,71,71,8.3,29,7729,73
|
||||||
|
Christendom College,Yes,81,72,51,33,71,139,3,8730,3600,400,800,92,92,9.3,17,10922,58
|
||||||
|
Christian Brothers University,Yes,880,520,224,16,42,1068,364,9300,3260,600,900,81,81,11.1,24,8129,63
|
||||||
|
Christopher Newport University,No,883,766,428,3,37,2910,1749,7860,4750,525,1889,80,82,21.2,16,4639,48
|
||||||
|
Claflin College,Yes,1196,697,499,21,47,959,13,4412,2460,500,1000,69,69,16.9,31,7083,21
|
||||||
|
Claremont McKenna College,Yes,1860,767,227,71,93,887,1,17000,6010,500,850,99,99,9.6,52,18443,87
|
||||||
|
Clark University,Yes,2887,2059,457,30,61,1928,296,17500,4200,500,950,94,95,10.5,35,11951,79
|
||||||
|
Clarke College,Yes,460,340,167,14,45,604,350,10740,3676,350,900,67,71,11,27,7963,74
|
||||||
|
Clarkson University,Yes,2174,1953,557,35,68,2332,53,15960,5580,700,1300,95,95,15.8,32,11659,77
|
||||||
|
Clemson University,No,8065,5257,2301,37,65,11755,770,8116,3610,800,1618,82,88,18,17,7597,73
|
||||||
|
Clinch Valley Coll. of the Univ. of Virginia,No,689,561,250,15,30,1125,422,7168,3689,600,1900,67,67,18.1,9,4417,46
|
||||||
|
Coe College,Yes,1006,742,275,29,60,1127,205,13925,4390,500,2200,73,86,12.7,32,10141,67
|
||||||
|
Coker College,Yes,604,452,295,15,47,690,222,9888,4502,400,1000,64,77,12.1,39,8741,75
|
||||||
|
Colby College,Yes,2848,1319,456,58,84,1720,35,18930,5590,500,1000,83,94,10.2,41,15954,91
|
||||||
|
Colgate University,Yes,4856,2492,727,46,75,2649,25,19510,5565,500,750,95,98,10.5,45,15494,93
|
||||||
|
College Misericordia,Yes,1432,888,317,29,58,1121,493,10860,5760,550,900,56,62,12.9,23,8604,96
|
||||||
|
College of Charleston,No,4772,3140,1265,22,55,6851,1200,6120,3460,666,2316,73,78,17.2,18,4776,51
|
||||||
|
College of Mount St. Joseph,Yes,798,620,238,14,41,1165,1232,9800,4430,400,1150,46,46,11.1,35,6889,100
|
||||||
|
College of Mount St. Vincent,Yes,946,648,177,23,46,707,432,11790,5770,500,1000,75,77,11.9,35,10015,83
|
||||||
|
College of Notre Dame,Yes,344,264,97,11,42,500,331,12600,5520,630,2250,77,80,10.4,7,9773,43
|
||||||
|
College of Notre Dame of Maryland,Yes,457,356,177,35,61,667,1983,11180,5620,600,700,64,64,11.5,32,7477,75
|
||||||
|
College of Saint Benedict,Yes,938,864,511,29,62,1715,103,12247,4221,500,600,70,88,13.1,26,8847,72
|
||||||
|
College of Saint Catherine,Yes,511,411,186,23,51,1692,562,12224,4440,450,1000,63,87,11.5,32,7315,77
|
||||||
|
College of Saint Elizabeth,Yes,444,359,122,34,53,493,968,10900,5250,380,1000,68,70,11.4,23,9447,78
|
||||||
|
College of Saint Rose,Yes,983,664,249,23,57,1698,894,9990,5666,800,1500,66,71,14.3,28,6084,64
|
||||||
|
College of Santa Fe,Yes,546,447,189,16,42,873,683,11138,4138,600,1200,40,74,14,7,8820,80
|
||||||
|
College of St. Joseph,Yes,141,118,55,12,21,201,173,8300,4850,450,1300,53,53,9.5,19,6936,76
|
||||||
|
College of St. Scholastica,Yes,672,596,278,29,60,1350,275,11844,3696,450,1146,54,76,11.6,33,8996,72
|
||||||
|
College of the Holy Cross,Yes,2994,1691,659,70,95,2675,22,18000,6300,400,900,92,96,11.3,55,12138,95
|
||||||
|
College of William and Mary,No,7117,3106,1217,68,88,5186,134,11720,4298,600,800,89,92,12.1,31,9534,93
|
||||||
|
College of Wooster,Yes,2100,1883,553,29,65,1704,1,16240,4690,500,500,84,96,11.1,43,14140,69
|
||||||
|
Colorado College,Yes,3207,1577,490,56,87,1892,7,17142,4190,450,1200,85,97,11.3,51,14664,84
|
||||||
|
Colorado State University,No,9478,6312,2194,29,65,15646,1829,8412,4180,470,1800,87,89,19.2,10,7850,59
|
||||||
|
Columbia College MO,Yes,314,158,132,10,28,690,5346,8294,3700,400,900,87,87,15.3,2,5015,37
|
||||||
|
Columbia College,Yes,737,614,242,21,67,968,237,10425,3975,500,1500,61,77,14.7,34,8693,76
|
||||||
|
Columbia University,Yes,6756,1930,871,78,96,3376,55,18624,6664,550,300,97,98,5.9,21,30639,99
|
||||||
|
Concordia College at St. Paul,Yes,281,266,139,13,29,1049,181,10500,3750,450,950,69,75,12.8,18,6955,45
|
||||||
|
Concordia Lutheran College,Yes,232,216,106,16,34,534,172,6900,3800,450,1825,67,76,12.1,9,6875,42
|
||||||
|
Concordia University CA,Yes,688,497,144,30,75,641,101,10800,4440,570,1515,55,60,13.1,13,8415,55
|
||||||
|
Concordia University,Yes,528,403,186,22,56,1168,145,9216,4191,400,1000,56,64,12.1,13,7309,75
|
||||||
|
Connecticut College,Yes,3035,1546,438,42,93,1630,232,18740,6300,600,500,86,95,10.7,40,14773,91
|
||||||
|
Converse College,Yes,440,407,149,35,70,643,80,12050,3700,500,900,63,76,10.2,31,10965,75
|
||||||
|
Cornell College,Yes,1538,1329,383,33,68,1140,10,15248,4323,550,800,71,76,12.2,31,10340,64
|
||||||
|
Creighton University,Yes,2967,2836,876,30,60,3450,644,10628,4372,650,2055,85,90,6.5,32,22906,85
|
||||||
|
Culver-Stockton College,Yes,1576,1110,274,24,55,992,112,8000,3700,400,500,51,52,14.1,28,5807,51
|
||||||
|
Cumberland College,Yes,995,789,398,26,47,1306,122,6230,3526,400,600,42,44,13,4,8189,63
|
||||||
|
D'Youville College,Yes,866,619,157,18,47,1074,336,8920,4310,680,1320,68,68,14.6,42,6898,46
|
||||||
|
Dana College,Yes,504,482,185,10,36,550,84,9130,3322,450,1450,46,51,12.6,25,8686,54
|
||||||
|
Daniel Webster College,Yes,585,508,153,12,30,460,536,12292,4934,500,500,61,61,22.2,10,8643,72
|
||||||
|
Dartmouth College,Yes,8587,2273,1087,87,99,3918,32,19545,6070,550,1100,95,99,4.7,49,29619,98
|
||||||
|
Davidson College,Yes,2373,956,452,77,96,1601,6,17295,5070,600,1011,95,97,12,46,17581,94
|
||||||
|
Defiance College,Yes,571,461,174,10,26,645,283,10850,3670,400,1159,58,60,12.8,19,7505,56
|
||||||
|
Delta State University,No,967,945,459,15,48,2806,538,4528,1880,500,1200,49,63,17.1,16,5113,58
|
||||||
|
Denison University,Yes,2762,2279,533,32,60,1835,14,16900,4720,500,600,88,97,11.6,45,12423,81
|
||||||
|
DePauw University,Yes,1994,1656,495,50,80,1983,36,14300,5020,550,950,78,94,11.1,31,11525,82
|
||||||
|
Dickinson College,Yes,3014,2539,487,31,68,1889,62,18700,5000,595,1250,87,94,11.2,39,13861,87
|
||||||
|
Dickinson State University,No,434,412,319,10,30,1376,237,4486,2146,600,2000,50,64,16.5,28,4525,46
|
||||||
|
Dillard University,Yes,1998,1376,651,41,88,1539,45,6700,3650,500,2307,52,52,14.1,12,7566,61
|
||||||
|
Doane College,Yes,793,709,244,20,47,1022,411,9570,3000,400,1000,67,72,15.1,42,6852,60
|
||||||
|
Dominican College of Blauvelt,Yes,360,329,108,4,19,756,863,8310,5500,600,1800,43,43,12.7,5,5480,54
|
||||||
|
Dordt College,Yes,604,562,328,25,50,1048,56,9800,2650,450,2800,61,60,12.5,17,7325,87
|
||||||
|
Dowling College,Yes,1011,829,410,9,33,1059,2458,9000,3100,450,1413,77,78,12.4,7,11178,42
|
||||||
|
Drake University,Yes,2799,2573,839,34,65,3322,726,13420,4770,560,1675,88,93,15,24,9473,77
|
||||||
|
Drew University,Yes,2153,1580,321,56,84,1192,87,18432,5616,520,660,93,97,10.2,28,14907,83
|
||||||
|
Drury College,Yes,700,650,314,33,66,1065,48,8730,3523,500,750,82,92,13.2,35,9303,67
|
||||||
|
Duke University,Yes,13789,3893,1583,90,98,6188,53,18590,5950,625,1162,95,96,5,44,27206,97
|
||||||
|
Earlham College,Yes,1358,1006,274,35,63,1028,13,15036,4056,600,600,90,94,10.6,46,14634,78
|
||||||
|
East Carolina University,No,9274,6362,2435,14,44,13171,1687,7248,3240,500,1700,74,78,13.2,18,9002,58
|
||||||
|
East Tennessee State University,No,3330,2730,1303,15,36,6706,2640,5800,3000,600,2200,73,75,14,9,9825,42
|
||||||
|
East Texas Baptist University,Yes,379,341,265,10,36,1050,151,4950,2780,530,1500,62,62,15.7,7,5619,38
|
||||||
|
Eastern College,Yes,458,369,165,16,42,1057,355,11190,4800,450,1230,60,60,13.6,22,8135,54
|
||||||
|
Eastern Connecticut State University,No,2172,1493,564,14,50,2766,1531,5962,4316,650,500,71,76,16.9,14,5719,50
|
||||||
|
Eastern Illinois University,No,5597,4253,1565,12,38,9161,845,5710,3066,120,1730,62,71,16.2,5,5682,76
|
||||||
|
Eastern Mennonite College,Yes,486,440,227,19,48,903,59,9650,3800,600,1300,46,65,11.4,29,10188,82
|
||||||
|
Eastern Nazarene College,Yes,516,409,200,17,40,1238,30,8770,3500,450,700,58,58,17.3,17,6430,70
|
||||||
|
Eckerd College,Yes,1422,1109,366,33,65,1363,23,15360,4080,600,1000,82,89,12.8,26,15003,59
|
||||||
|
Elizabethtown College,Yes,2417,1843,426,36,70,1476,299,14190,4400,500,750,65,68,12.8,25,9815,81
|
||||||
|
Elmira College,Yes,1457,1045,345,27,50,1109,502,14990,4980,450,550,77,98,21.5,21,7502,64
|
||||||
|
Elms College,Yes,245,208,125,23,46,544,436,11800,4765,450,1700,71,71,11.3,21,8952,86
|
||||||
|
Elon College,Yes,3624,2786,858,11,39,2933,334,9100,3883,490,1777,70,74,18.9,34,6329,63
|
||||||
|
Embry Riddle Aeronautical University,Yes,3151,2584,958,14,40,4772,856,7800,3750,570,3020,37,43,16.5,4,12878,44
|
||||||
|
Emory & Henry College,Yes,765,646,226,30,60,809,32,8578,4408,700,1600,79,88,13.9,51,8061,82
|
||||||
|
Emory University,Yes,8506,4168,1236,76,97,5544,192,17600,6000,600,870,97,98,5,28,28457,96
|
||||||
|
Emporia State University,No,1256,1256,853,43,79,3957,588,5401,3144,450,1888,72,75,19.3,4,5527,50
|
||||||
|
Erskine College,Yes,659,557,167,47,74,532,35,10485,3840,475,1246,76,80,13.5,47,7527,67
|
||||||
|
Eureka College,Yes,560,454,113,36,56,484,16,10955,3450,330,670,62,87,10.6,31,9552,53
|
||||||
|
Evergreen State College,No,1801,1101,438,14,50,3065,363,6297,4600,600,1323,75,78,18.1,14,8355,68
|
||||||
|
Fairfield University,Yes,4784,3346,781,30,66,2984,1037,15000,6200,700,1100,86,90,15.1,30,11220,94
|
||||||
|
Fayetteville State University,No,1455,1064,452,1,16,2632,617,6806,2550,350,766,75,75,15.1,10,6972,24
|
||||||
|
Ferrum College,Yes,1339,1107,336,12,36,1051,82,9400,4200,500,1600,53,58,12.5,9,7967,22
|
||||||
|
Flagler College,Yes,1415,714,338,18,52,1345,44,5120,3200,500,2140,52,60,18.1,9,3930,69
|
||||||
|
Florida Institute of Technology,Yes,1947,1580,523,39,74,1863,233,13900,4140,750,1500,90,90,10.6,7,8923,57
|
||||||
|
Florida International University,No,3306,2079,1071,42,89,10208,9310,6597,2494,800,3028,81,96,13.9,20,6722,66
|
||||||
|
Florida Southern College,Yes,1381,1040,374,20,44,1506,970,8025,4865,400,650,65,74,17.4,10,6339,68
|
||||||
|
Florida State University,No,11651,8683,3023,50,90,18906,3242,6680,4060,600,1020,80,89,23.1,15,7250,58
|
||||||
|
Fontbonne College,Yes,291,245,126,16,49,981,337,8390,4100,350,1500,45,55,21.5,24,4607,62
|
||||||
|
Fordham University,Yes,4200,2874,942,30,55,4740,1646,14235,6965,600,1735,86,97,14.4,14,10864,80
|
||||||
|
Fort Lewis College,No,3440,2823,1123,16,35,3793,486,6198,3320,500,2500,89,97,19.1,6,4362,46
|
||||||
|
Francis Marion University,No,1801,1655,819,13,38,3224,436,5840,3138,400,2430,76,76,19.1,8,5039,43
|
||||||
|
Franciscan University of Steubenville,Yes,553,452,228,22,49,1301,242,9650,4400,600,1000,57,69,14.9,8,6336,83
|
||||||
|
Franklin College,Yes,804,632,281,29,72,840,68,10390,4040,525,1345,54,78,12.5,37,11751,60
|
||||||
|
Franklin Pierce College,Yes,5187,4471,446,3,14,1818,1197,13320,4630,500,800,50,56,17.6,16,6418,51
|
||||||
|
Freed-Hardeman University,Yes,895,548,314,20,54,1174,50,5500,3340,600,1600,68,76,16.1,13,6078,62
|
||||||
|
Fresno Pacific College,Yes,346,274,146,51,87,704,63,9900,3670,630,1818,59,59,10.5,14,8095,54
|
||||||
|
Furman University,Yes,2161,1951,685,56,82,2371,175,13440,4048,600,1250,92,95,13.5,28,12940,82
|
||||||
|
Gannon University,Yes,2464,1908,678,24,57,2693,691,10970,4280,500,1380,47,51,13.3,18,7711,65
|
||||||
|
Gardner Webb University,Yes,1110,930,332,18,36,1603,374,8180,4270,500,500,65,58,15.2,12,5664,29
|
||||||
|
Geneva College,Yes,668,534,237,19,39,1306,258,9476,4820,500,1100,67,67,20.1,26,6786,74
|
||||||
|
George Fox College,Yes,809,726,294,27,52,1271,43,12500,4130,400,1050,53,53,13.5,22,7136,52
|
||||||
|
George Mason University,No,5653,4326,1727,17,29,9528,3822,10800,4840,580,1050,93,96,19.3,7,6751,46
|
||||||
|
George Washington University,Yes,7875,5062,1492,38,71,5471,1470,17450,6328,700,950,92,93,7.6,15,14745,72
|
||||||
|
Georgetown College,Yes,727,693,286,30,55,1063,48,8100,3950,550,550,73,76,13.3,28,7508,55
|
||||||
|
Georgetown University,Yes,11115,2881,1390,71,93,5881,406,18300,7131,670,1700,91,92,7.2,27,19635,95
|
||||||
|
Georgia Institute of Technology,No,7837,4527,2276,89,99,8528,654,6489,4438,795,1164,92,92,19.3,33,11271,70
|
||||||
|
Georgia State University,No,3793,2341,1238,9,24,7732,9054,6744,2655,720,3450,87,89,19,10,7762,34
|
||||||
|
Georgian Court College,Yes,348,281,127,12,52,1095,785,9150,3950,500,800,56,59,12.2,27,7348,76
|
||||||
|
Gettysburg College,Yes,3596,2466,575,42,78,1944,46,19964,4328,500,500,94,95,12.1,32,14720,83
|
||||||
|
Goldey Beacom College,Yes,633,468,284,10,27,823,963,6120,2985,531,1830,25,25,27.6,4,6081,36
|
||||||
|
Gonzaga University,Yes,1886,1524,526,31,67,2523,296,13000,4450,600,2400,78,90,14.7,32,9553,69
|
||||||
|
Gordon College,Yes,674,565,282,25,54,1151,39,12200,4070,400,1200,73,82,14.2,32,9226,66
|
||||||
|
Goshen College,Yes,440,396,221,26,51,910,166,9420,3730,600,1230,51,56,9.9,46,10270,72
|
||||||
|
Goucher College,Yes,1151,813,248,40,64,850,80,15588,6174,500,1200,78,90,9.2,34,16623,77
|
||||||
|
Grace College and Seminary,Yes,548,428,167,18,46,618,113,8958,3670,300,1000,53,59,15.3,26,9798,64
|
||||||
|
Graceland College,Yes,555,414,242,14,41,996,2281,9100,3100,550,880,51,61,23.6,24,5609,47
|
||||||
|
Grand Valley State University,No,5165,3887,1561,20,60,8234,2619,6108,3800,500,1000,64,66,20.6,9,5063,57
|
||||||
|
Green Mountain College,Yes,780,628,198,7,20,545,42,11750,2700,400,850,77,83,14,24,6475,76
|
||||||
|
Greensboro College,Yes,608,494,176,10,31,649,314,8330,3770,550,1300,64,80,13,31,7949,39
|
||||||
|
Greenville College,Yes,510,387,194,20,46,771,53,10310,4530,400,800,57,61,14.3,16,8222,60
|
||||||
|
Grinnell College,Yes,2039,1389,432,56,91,1333,30,15688,4618,400,400,88,92,9.5,54,18979,83
|
||||||
|
Grove City College,Yes,2491,1110,573,57,88,2213,35,5224,3048,525,350,65,65,18.4,18,4957,100
|
||||||
|
Guilford College,Yes,1202,1054,326,18,44,1410,299,13404,5160,450,1050,78,86,15.6,30,9114,65
|
||||||
|
Gustavus Adolphus College,Yes,1709,1385,634,36,72,2281,50,14125,3600,400,700,79,89,12.5,58,9907,80
|
||||||
|
Gwynedd Mercy College,Yes,380,237,104,30,56,716,1108,11000,5550,500,500,36,41,7.8,22,7483,96
|
||||||
|
Hamilton College,Yes,3140,1783,454,40,82,1646,24,19700,5050,300,800,91,96,9.6,60,17761,91
|
||||||
|
Hamline University,Yes,1006,825,328,34,73,1362,102,13252,4194,450,550,89,93,13,33,10296,65
|
||||||
|
Hampden - Sydney College,Yes,817,644,307,20,40,945,1,13218,4773,660,600,95,97,13.3,53,12263,69
|
||||||
|
Hampton University,Yes,7178,3755,1433,25,63,4623,740,7161,3518,600,2000,60,64,14,9,6791,70
|
||||||
|
Hanover College,Yes,1006,837,317,33,65,1024,15,8200,3485,500,1200,84,84,10.6,26,9248,64
|
||||||
|
Hardin-Simmons University,Yes,467,424,350,16,40,1365,334,6300,2980,700,2140,75,79,13.7,10,7054,38
|
||||||
|
Harding University,Yes,1721,1068,806,35,75,3128,213,5504,3528,700,910,71,77,17.7,37,6466,73
|
||||||
|
Hartwick College,Yes,2083,1725,430,22,49,1464,67,17480,4780,500,700,75,87,12.3,32,11625,73
|
||||||
|
Harvard University,Yes,13865,2165,1606,90,100,6862,320,18485,6410,500,1920,97,97,9.9,52,37219,100
|
||||||
|
Harvey Mudd College,Yes,1377,572,178,95,100,654,5,17230,6690,700,900,100,100,8.2,46,21569,100
|
||||||
|
Hastings College,Yes,817,708,262,22,52,935,37,9376,3272,500,1902,57,63,13,17,7335,52
|
||||||
|
Hendrix College,Yes,823,721,274,52,87,954,6,8800,3195,500,1200,82,99,13.1,26,8588,63
|
||||||
|
Hillsdale College,Yes,920,745,347,35,66,1133,42,11090,4700,400,750,80,80,12,31,12639,79
|
||||||
|
Hiram College,Yes,922,729,244,37,66,1000,275,14067,4560,400,1000,75,95,10.6,34,12165,79
|
||||||
|
Hobart and William Smith Colleges,Yes,2688,2081,500,25,53,1792,5,19029,5841,600,600,99,99,12.1,37,13040,79
|
||||||
|
Hofstra University,Yes,7428,5860,1349,25,63,6534,1350,11600,5920,1000,1000,81,90,13.9,10,10093,60
|
||||||
|
Hollins College,Yes,602,498,215,26,58,795,74,13470,5515,500,850,78,91,11.1,48,13957,72
|
||||||
|
Hood College,Yes,699,565,176,36,64,710,399,13960,6040,450,690,82,88,14.4,34,12434,72
|
||||||
|
Hope College,Yes,1712,1483,624,37,69,2505,208,12275,4341,465,1100,72,81,12.5,40,9284,72
|
||||||
|
Houghton College,Yes,949,786,302,30,70,1210,26,9990,3550,500,1500,85,90,15,24,8187,67
|
||||||
|
Huntingdon College,Yes,608,520,127,26,47,538,126,8080,3920,500,1100,63,72,11.4,9,7703,44
|
||||||
|
Huntington College,Yes,450,430,125,20,46,488,43,9950,3920,300,1300,76,76,11.8,25,9466,47
|
||||||
|
Huron University,Yes,600,197,124,3,9,392,69,7260,3090,600,1840,31,35,12.9,4,9249,21
|
||||||
|
Husson College,Yes,723,652,361,10,30,951,706,7800,4000,350,1500,36,44,22,4,4923,84
|
||||||
|
Illinois Benedictine College,Yes,607,558,269,22,47,1222,519,10500,4348,650,1500,81,91,11.6,29,8324,75
|
||||||
|
Illinois College,Yes,894,787,262,28,63,909,28,8050,3850,600,1000,75,75,15.6,30,7348,52
|
||||||
|
Illinois Institute of Technology,Yes,1756,1360,478,42,77,1911,626,14550,4620,500,700,80,88,12.3,26,12851,56
|
||||||
|
Illinois State University,No,8681,6695,2408,10,35,15701,1823,7799,3403,537,2605,77,84,21,16,5569,54
|
||||||
|
Illinois Wesleyan University,Yes,3050,1342,471,55,86,1818,23,14360,4090,400,650,77,92,12.9,34,9605,83
|
||||||
|
Immaculata College,Yes,268,253,103,16,44,494,1305,10000,5364,500,1000,56,64,11.2,33,7305,69
|
||||||
|
Incarnate Word College,Yes,1163,927,386,16,49,1685,556,8840,4689,750,2775,67,69,11.4,21,6095,95
|
||||||
|
Indiana State University,No,5659,4761,3147,10,31,8596,1949,6892,3706,600,2500,72,76,16.6,8,6996,40
|
||||||
|
Indiana University at Bloomington,No,16587,13243,5873,25,72,24763,2717,9766,3990,600,2000,77,88,21.3,24,8686,68
|
||||||
|
Indiana Wesleyan University,Yes,735,423,366,20,48,2448,707,9210,3782,700,1000,49,51,39.8,15,6562,34
|
||||||
|
Iona College,Yes,4892,3530,913,13,33,3906,1446,10690,6790,570,1150,66,83,16,14,8107,66
|
||||||
|
Iowa State University,No,8427,7424,3441,26,59,18676,1715,7550,3224,640,2055,81,88,19.2,22,8420,65
|
||||||
|
Ithaca College,Yes,7259,5526,1368,23,52,5612,166,14424,6192,634,1000,58,79,11.5,25,9812,75
|
||||||
|
James Madison University,No,11223,5285,2082,32,72,9652,742,7994,4544,500,732,77,81,17.9,29,5212,98
|
||||||
|
Jamestown College,Yes,472,410,262,14,41,9950,71,7620,3050,400,400,51,53,17,21,3186,54
|
||||||
|
Jersey City State College,No,2957,1423,691,10,30,3817,1394,3946,4800,400,1500,63,67,14.9,10,8367,26
|
||||||
|
John Brown University,Yes,605,405,284,24,53,961,99,6398,3672,400,1350,68,68,13.3,19,8118,75
|
||||||
|
John Carroll University,Yes,2421,2109,820,27,57,3168,392,11700,5550,600,450,89,90,14.5,28,7738,89
|
||||||
|
Johns Hopkins University,Yes,8474,3446,911,75,94,3566,1569,18800,6740,500,1040,96,97,3.3,38,56233,90
|
||||||
|
Johnson State College,No,833,669,279,3,13,1224,345,7656,4690,500,624,80,91,14.4,15,6564,36
|
||||||
|
Judson College,Yes,313,228,137,10,30,552,67,9414,4554,500,1700,34,55,10.6,30,7840,56
|
||||||
|
Juniata College,Yes,1005,859,298,36,55,1075,43,14850,4460,450,420,97,97,12.7,37,12067,80
|
||||||
|
Kansas State University,No,5880,4075,2833,25,55,14914,2246,6995,3120,600,2000,76,86,18.5,22,6122,54
|
||||||
|
Kansas Wesleyan University,Yes,589,575,148,16,40,474,258,8400,3250,500,1400,63,55,12.4,14,6535,68
|
||||||
|
Keene State College,No,3121,2446,822,5,19,3480,776,7870,4157,500,1150,73,73,16.1,13,6195,61
|
||||||
|
Kentucky Wesleyan College,Yes,584,497,175,20,49,662,121,8000,4150,500,1300,57,65,11.3,32,7058,62
|
||||||
|
Kenyon College,Yes,2212,1538,408,44,75,1445,1,19240,3690,750,480,95,95,11.1,46,14067,88
|
||||||
|
Keuka College,Yes,461,381,174,10,43,738,55,9600,4550,600,750,55,94,13.3,43,7863,51
|
||||||
|
King's College,Yes,1456,1053,381,20,45,500,541,10910,5160,400,1795,66,72,15.6,37,7649,87
|
||||||
|
King College,Yes,355,300,142,34,65,509,44,8664,3350,600,3000,65,68,10.7,25,8954,65
|
||||||
|
Knox College,Yes,1040,845,286,48,77,967,24,15747,4062,400,800,88,95,12.7,33,13224,79
|
||||||
|
La Roche College,Yes,361,321,185,10,41,650,819,8842,4782,600,1100,57,73,14.2,14,7022,52
|
||||||
|
La Salle University,Yes,2929,1834,622,20,56,2738,1662,12600,5610,450,3160,90,90,15.1,9,9084,84
|
||||||
|
Lafayette College,Yes,4010,2402,572,36,59,2018,226,18730,5740,600,1000,93,96,10.5,38,15365,92
|
||||||
|
LaGrange College,Yes,544,399,177,15,35,600,363,6987,3585,750,1500,77,83,12.5,12,9067,75
|
||||||
|
Lake Forest College,Yes,979,638,271,31,70,968,20,16880,3970,920,1320,91,94,10.7,19,15687,77
|
||||||
|
Lakeland College,Yes,497,452,231,24,47,887,1957,9400,4005,500,1000,49,65,17.2,25,4054,57
|
||||||
|
Lamar University,No,2336,1725,1043,10,27,5438,4058,4752,3040,508,1463,48,82,18.4,12,5879,26
|
||||||
|
Lambuth University,Yes,831,538,224,15,35,840,325,5170,3430,600,1590,61,61,16.1,10,5531,60
|
||||||
|
Lander University,No,1166,1009,510,9,33,2074,341,4938,2987,528,1702,67,77,17,11,6119,51
|
||||||
|
Lawrence University,Yes,1243,947,324,50,77,1129,74,17163,3891,525,975,76,92,10.1,57,13965,77
|
||||||
|
Le Moyne College,Yes,1470,1199,425,21,76,1820,558,11040,4840,400,900,89,92,13.3,28,8118,94
|
||||||
|
Lebanon Valley College,Yes,1386,1060,320,28,56,965,502,13850,4755,400,1125,84,84,12.3,30,8196,85
|
||||||
|
Lehigh University,Yes,6397,4304,1092,40,84,4298,132,18700,5580,750,1130,96,99,12.5,43,14665,91
|
||||||
|
Lenoir-Rhyne College,Yes,979,743,259,25,46,1188,166,10100,4000,400,1000,88,92,12,20,8539,66
|
||||||
|
Lesley College,Yes,244,198,82,12,33,1134,336,11700,5300,550,805,71,88,27.8,18,8694,58
|
||||||
|
LeTourneau University,Yes,477,417,204,29,54,1532,77,8840,4240,600,1400,58,70,20.8,23,6863,56
|
||||||
|
Lewis and Clark College,Yes,2774,2092,482,35,64,1763,59,15800,4790,450,950,97,98,12.3,21,12999,69
|
||||||
|
Lewis University,Yes,1154,1050,395,12,31,2192,1423,10560,4520,500,1200,36,48,14.3,10,7701,61
|
||||||
|
Lincoln Memorial University,Yes,787,562,363,21,55,925,605,5950,2890,600,1300,67,72,14.6,35,5177,53
|
||||||
|
Lincoln University,No,1660,1091,326,15,41,1196,33,4818,3400,350,1400,71,72,12.6,8,10912,45
|
||||||
|
Lindenwood College,Yes,810,484,356,6,33,2155,191,9200,4800,1000,4200,65,85,24.1,9,3480,100
|
||||||
|
Linfield College,Yes,1561,1188,458,48,72,1548,840,13380,4210,500,900,89,91,17.8,34,8747,81
|
||||||
|
Livingstone College,Yes,900,473,217,22,47,621,11,4400,3400,800,900,53,93,10.4,16,9268,92
|
||||||
|
Lock Haven University of Pennsylvania,No,3570,2215,651,17,41,3390,325,7352,3620,225,500,47,55,16.1,14,6374,63
|
||||||
|
Longwood College,No,2747,1870,724,12,47,2874,118,7920,3962,550,2200,74,80,18.4,23,5553,62
|
||||||
|
Loras College,Yes,1641,1283,527,20,39,1663,170,11200,4000,500,1200,61,62,14.2,24,7578,70
|
||||||
|
Louisiana College,Yes,2013,1053,212,33,61,912,158,5150,3036,500,1655,64,74,10.5,11,7547,59
|
||||||
|
Louisiana State University at Baton Rouge,No,5996,4993,3079,29,57,16269,3757,5925,2980,600,2242,83,87,15.9,11,6741,37
|
||||||
|
Louisiana Tech University,No,2397,2144,1525,22,45,6720,1822,3957,2325,618,1656,66,77,20,13,4546,45
|
||||||
|
Loyola College,Yes,4076,3137,738,25,54,3010,184,12990,6300,600,900,86,88,14.7,27,9448,80
|
||||||
|
Loyola Marymount University,Yes,3768,2662,753,42,64,3558,436,13592,5916,545,1328,84,88,14.2,10,11677,84
|
||||||
|
Loyola University,Yes,1891,1698,719,24,80,2740,761,11100,5870,600,750,77,88,11.7,14,9456,53
|
||||||
|
Loyola University Chicago,Yes,3579,2959,868,25,55,5244,3417,11500,5330,700,2000,94,95,6.2,15,13009,65
|
||||||
|
Luther College,Yes,1549,1392,587,38,72,2269,85,13240,3560,600,400,73,85,13.8,38,8949,77
|
||||||
|
Lycoming College,Yes,1286,1005,363,16,37,1363,74,13900,4300,500,900,75,81,14,32,8024,72
|
||||||
|
Lynchburg College,Yes,1756,1500,366,3,21,1524,280,12450,5400,450,870,62,66,12.4,24,8832,70
|
||||||
|
Lyndon State College,No,535,502,223,6,20,959,150,7320,4640,500,600,48,65,12.6,15,7114,51
|
||||||
|
Macalester College,Yes,2939,1496,452,56,86,1723,113,15909,4772,500,700,85,91,11.9,37,14213,77
|
||||||
|
MacMurray College,Yes,740,558,177,12,29,628,63,9620,3750,550,950,49,55,10.8,33,10642,59
|
||||||
|
Malone College,Yes,874,758,428,21,46,1605,246,9858,3700,450,1200,42,45,17.6,16,4796,55
|
||||||
|
Manchester College,Yes,1004,802,239,23,63,909,51,10440,3850,525,1450,63,72,11.8,20,7940,64
|
||||||
|
Manhattan College,Yes,2432,1730,563,20,63,2578,254,12370,6800,500,1800,92,92,13.6,25,10062,79
|
||||||
|
Manhattanville College,Yes,962,750,212,21,54,830,150,14700,6550,450,400,97,97,11.3,24,11291,70
|
||||||
|
Mankato State University,No,3073,2672,1547,9,29,9649,1792,4300,2643,450,1660,57,68,19,11,5801,68
|
||||||
|
Marian College of Fond du Lac,Yes,824,670,337,15,41,1160,653,9400,3400,500,1100,37,37,8.4,21,5352,59
|
||||||
|
Marietta College,Yes,1611,960,342,27,60,1089,210,13850,3920,470,810,80,97,13.2,30,10223,96
|
||||||
|
Marist College,Yes,4731,3171,830,12,31,3557,658,10700,5925,550,1200,74,81,17.6,34,8408,69
|
||||||
|
Marquette University,Yes,5152,4600,1685,36,71,7016,804,11610,4760,600,1950,86,94,13.5,25,9982,77
|
||||||
|
Marshall University,Yes,4226,3666,2007,14,60,7703,2339,5094,4010,700,1560,77,86,16.6,10,6203,50
|
||||||
|
Mary Baldwin College,Yes,499,441,199,26,52,846,377,11200,7400,600,1300,66,79,6.8,50,10819,90
|
||||||
|
Mary Washington College,No,4350,2178,756,39,78,2997,736,6490,4942,650,2102,75,80,17.6,30,5358,84
|
||||||
|
Marymount College Tarrytown,Yes,478,327,117,9,34,731,370,11510,6450,575,1075,71,93,10.3,30,10502,77
|
||||||
|
Marymount Manhattan College,Yes,695,535,239,21,30,988,785,10200,7000,350,1100,63,76,11.7,20,10622,68
|
||||||
|
Marymount University,Yes,941,772,214,10,30,1247,776,11390,5280,500,750,77,82,10.6,17,8575,55
|
||||||
|
Maryville College,Yes,1464,888,176,26,52,624,128,11200,4208,500,1642,80,90,11.1,43,8317,51
|
||||||
|
Maryville University,Yes,549,397,169,26,51,1343,1751,9250,4550,425,1350,52,58,13.1,13,5925,61
|
||||||
|
Marywood College,Yes,1107,859,323,13,51,1452,402,11040,4500,600,700,65,76,11.8,30,9034,66
|
||||||
|
Massachusetts Institute of Technology,Yes,6411,2140,1078,96,99,4481,28,20100,5975,725,1600,99,99,10.1,35,33541,94
|
||||||
|
Mayville State University,No,233,233,153,5,12,658,58,4486,2516,600,1900,68,68,15.7,11,6971,51
|
||||||
|
McKendree College,Yes,1002,555,119,16,43,836,684,7680,3740,500,800,70,74,17.7,21,6652,52
|
||||||
|
McMurry University,Yes,578,411,187,25,50,880,477,6930,3452,400,1525,57,64,11,11,6383,32
|
||||||
|
McPherson College,Yes,420,293,93,11,32,336,80,7950,3750,600,2740,54,54,9.8,45,9754,48
|
||||||
|
Mercer University,Yes,2286,1668,564,37,70,2943,1260,11985,4081,400,1500,93,95,9.2,15,8995,91
|
||||||
|
Mercyhurst College,Yes,1557,1074,397,15,40,1805,433,9813,4050,425,1000,45,63,16.7,29,7307,78
|
||||||
|
Meredith College,Yes,857,772,376,25,58,1721,470,6720,3250,450,1520,77,82,13.9,33,6881,82
|
||||||
|
Merrimack College,Yes,1981,1541,514,18,36,1927,1084,12500,6200,375,1000,73,75,16.8,22,8707,80
|
||||||
|
Mesa State College,No,1584,1456,891,6,18,3471,911,5016,3798,540,2256,48,48,28.8,12,3871,59
|
||||||
|
Messiah College,Yes,1742,1382,607,30,64,2258,53,10300,5080,475,1200,68,75,14.1,30,7762,89
|
||||||
|
Miami University at Oxford,No,9239,7788,3290,35,39,13606,807,8856,3960,500,1382,81,89,17.6,20,7846,85
|
||||||
|
Michigan State University,No,18114,15096,6180,23,57,26640,4120,10658,3734,504,600,93,95,14,9,10520,71
|
||||||
|
Michigan Technological University,No,2618,2288,1032,42,77,5524,414,8127,3978,900,1200,82,82,17,25,7473,65
|
||||||
|
MidAmerica Nazarene College,Yes,331,331,225,15,36,1100,166,6840,3720,1100,4913,33,33,15.4,20,5524,49
|
||||||
|
Millersville University of Penn.,No,6011,3075,960,22,60,5146,1532,7844,3830,450,1258,72,74,16.8,20,7832,71
|
||||||
|
Milligan College,Yes,610,461,189,26,52,685,49,8200,3300,550,1000,63,69,12,16,8128,64
|
||||||
|
Millikin University,Yes,1444,1261,456,29,62,1788,95,11910,4378,450,965,60,77,11.4,25,8149,75
|
||||||
|
Millsaps College,Yes,905,834,319,32,61,1073,179,11320,4402,550,1350,82,89,12.7,38,11218,58
|
||||||
|
Milwaukee School of Engineering,Yes,1217,1088,496,36,69,1773,884,11505,3255,1000,2075,35,46,16.7,23,7140,67
|
||||||
|
Mississippi College,Yes,594,385,307,36,57,1695,721,5580,2830,600,700,77,79,16.5,18,6170,61
|
||||||
|
Mississippi State University,No,4255,3277,1609,18,57,10094,1621,9866,3084,480,1479,77,77,15.9,20,6223,53
|
||||||
|
Mississippi University for Women,No,480,405,380,19,46,1673,1014,4386,2217,600,1500,49,54,15.8,8,5704,63
|
||||||
|
Missouri Southern State College,No,1576,1326,913,13,50,3689,2200,3840,2852,200,400,52,54,20.3,9,4172,100
|
||||||
|
Missouri Valley College,Yes,1310,983,316,5,35,1057,175,8550,5050,400,900,35,67,17.4,16,4333,27
|
||||||
|
Monmouth College IL,Yes,601,503,204,28,57,671,11,13000,4100,400,460,91,91,11.6,43,11087,56
|
||||||
|
Monmouth College,Yes,2707,1881,478,14,34,1893,847,12480,5290,530,1740,70,85,14.2,15,9492,54
|
||||||
|
Montana College of Mineral Sci. & Tech.,No,572,544,320,45,72,1470,416,6073,3400,550,1400,71,71,16.4,31,6112,74
|
||||||
|
Montana State University,No,3500,2836,1779,15,42,8730,993,5552,3710,550,2300,75,83,17.6,8,6324,37
|
||||||
|
Montclair State University,No,5220,2128,865,19,53,6411,3186,3648,4834,700,950,82,87,21.5,9,6717,58
|
||||||
|
Montreat-Anderson College,Yes,263,223,103,10,24,316,20,8438,3372,500,2958,42,50,11.1,4,11989,15
|
||||||
|
Moorhead State University,No,2442,2164,1189,12,37,5983,1075,4426,2664,600,1000,76,81,18.1,19,4795,60
|
||||||
|
Moravian College,Yes,1232,955,303,23,58,1241,485,14990,4730,550,1250,86,92,15.2,28,9566,74
|
||||||
|
Morehouse College,Yes,3708,1678,722,41,66,2852,153,7050,5490,250,600,71,74,17.8,10,8122,83
|
||||||
|
Morningside College,Yes,586,533,239,16,36,950,228,10520,3678,500,1000,48,68,13,32,8111,56
|
||||||
|
Morris College,Yes,882,730,330,2,13,926,12,4515,2550,850,2100,53,60,18.6,34,6990,60
|
||||||
|
Mount Holyoke College,Yes,1800,1314,526,47,79,1891,40,19300,5700,750,750,79,91,9,51,18359,84
|
||||||
|
Mount Marty College,Yes,279,276,126,17,37,600,435,6844,2980,500,500,45,55,11.7,38,5073,44
|
||||||
|
Mount Mary College,Yes,235,217,121,12,32,931,487,8950,3119,550,1125,51,51,10.7,26,7016,78
|
||||||
|
Mount Mercy College,Yes,368,317,159,20,49,806,542,10500,3555,500,2285,44,50,11.3,30,6695,64
|
||||||
|
Mount Saint Clare College,Yes,325,284,95,16,33,364,88,9900,3650,500,1200,32,37,13.6,43,6525,21
|
||||||
|
Mount Saint Mary's College,Yes,1321,1159,328,15,36,1243,79,12850,6200,550,900,77,82,12.8,36,8536,80
|
||||||
|
Mount Saint Mary College,Yes,1170,695,238,14,48,1170,429,7470,4600,250,1400,74,75,15.3,23,6898,88
|
||||||
|
Mount St. Mary's College,Yes,657,537,113,37,90,1039,466,12474,5678,630,1278,53,71,11.9,19,10613,72
|
||||||
|
Mount Union College,Yes,1310,1086,458,26,61,1365,144,12250,3530,400,1150,85,87,16.7,35,7215,81
|
||||||
|
Mount Vernon Nazarene College,Yes,510,485,334,18,36,1114,94,7400,3346,600,600,57,57,19.8,7,6869,58
|
||||||
|
Muhlenberg College,Yes,2519,1836,462,30,61,1656,352,16975,4565,600,850,76,86,12.8,39,10888,83
|
||||||
|
Murray State University,No,2225,1910,1190,29,55,5968,955,4738,3110,700,940,72,76,20.2,27,5972,52
|
||||||
|
Muskingum College,Yes,1109,922,375,24,46,1115,70,13240,3914,600,800,73,85,13.4,27,9333,73
|
||||||
|
National-Louis University,Yes,513,347,279,23,48,2508,505,9090,4500,650,500,62,65,18.3,2,7905,71
|
||||||
|
Nazareth College of Rochester,Yes,947,798,266,36,68,1274,471,10850,5150,550,800,77,93,13.6,24,8797,61
|
||||||
|
New Jersey Institute of Technology,No,1879,1216,483,27,62,3311,1646,8832,5376,700,1850,92,98,13.5,19,12529,72
|
||||||
|
New Mexico Institute of Mining and Tech.,No,787,601,233,40,73,1017,411,5376,3214,600,1100,99,100,13.7,11,9241,34
|
||||||
|
New York University,Yes,13594,7244,2505,70,86,12408,2814,17748,7262,450,1000,87,98,7.8,16,21227,71
|
||||||
|
Newberry College,Yes,872,722,154,14,36,601,36,10194,2600,500,1500,57,63,11.4,32,5788,83
|
||||||
|
Niagara University,Yes,2220,1796,467,65,99,1919,334,10320,4762,450,650,68,100,14.2,20,7788,65
|
||||||
|
North Adams State College,No,1563,1005,240,1,19,1380,136,5542,4330,500,1000,65,71,14.2,17,6562,57
|
||||||
|
North Carolina A. & T. State University,No,4809,3089,1429,12,33,6162,871,6806,1780,600,1651,72,72,16.7,9,7090,44
|
||||||
|
North Carolina State University at Raleigh,No,10634,7064,3176,39,78,16505,5481,8400,6540,600,1300,92,98,17.5,21,9670,62
|
||||||
|
North Carolina Wesleyan College,Yes,812,689,195,7,24,646,84,8242,4230,600,1295,77,77,12.7,11,10090,52
|
||||||
|
North Central College,Yes,1127,884,308,30,64,1310,766,11718,7398,450,1800,73,87,16.4,33,8871,76
|
||||||
|
North Dakota State University,No,2968,2297,1610,13,47,7368,1128,5834,2744,600,2000,79,83,17,24,6310,42
|
||||||
|
North Park College,Yes,465,361,176,19,39,879,156,12580,4345,400,970,76,79,13.1,24,10889,74
|
||||||
|
Northeast Missouri State University,No,6040,4577,1620,36,72,5640,266,4856,3416,400,1100,69,72,15.7,13,6601,76
|
||||||
|
Northeastern University,Yes,11901,8492,2517,16,42,11160,10221,13380,7425,600,1750,73,82,12.9,17,9563,46
|
||||||
|
Northern Arizona University,No,5891,4931,1973,23,48,11249,2682,6746,3728,620,2342,78,83,21.7,7,6157,41
|
||||||
|
Northern Illinois University,No,10706,7219,2397,12,37,14826,1979,7799,3296,470,1750,73,78,17.3,11,6086,56
|
||||||
|
Northwest Missouri State University,No,2729,2535,1257,8,29,4787,472,3735,3136,250,1630,62,65,21.7,23,5284,54
|
||||||
|
Northwest Nazarene College,Yes,616,514,385,29,52,1115,60,9840,2820,450,822,59,59,14.8,20,6261,58
|
||||||
|
Northwestern College,Yes,860,811,366,22,56,1040,52,9900,3075,300,1800,68,68,14.9,34,6357,68
|
||||||
|
Northwestern University,Yes,12289,5200,1902,85,98,7450,45,16404,5520,759,1585,96,100,6.8,25,26385,92
|
||||||
|
Norwich University,Yes,1743,1625,626,8,29,1862,382,14134,5270,500,800,71,74,13.1,22,9209,63
|
||||||
|
Notre Dame College,Yes,379,324,107,15,37,500,311,9990,4900,400,600,44,47,12.1,26,4948,33
|
||||||
|
Oakland University,No,3041,2581,1173,16,56,6441,3982,9114,4030,400,650,88,90,19.7,13,6637,53
|
||||||
|
Oberlin College,Yes,4778,2767,678,50,89,2587,120,19670,5820,575,1119,77,96,10.1,47,16593,83
|
||||||
|
Occidental College,Yes,2324,1319,370,52,81,1686,35,16560,5140,558,1152,91,93,10.5,30,16196,79
|
||||||
|
Oglethorpe University,Yes,792,649,186,56,87,769,377,12900,4340,600,4110,91,95,13.1,27,8568,67
|
||||||
|
Ohio Northern University,Yes,2936,2342,669,35,62,2502,66,15990,4080,600,825,73,78,14.5,31,9979,83
|
||||||
|
Ohio University,No,11023,8298,3183,21,54,14861,1310,7629,4095,550,2300,79,87,20.4,13,8811,64
|
||||||
|
Ohio Wesleyan University,Yes,2190,1700,458,36,65,1780,48,16732,5650,550,550,93,93,12.1,32,12011,75
|
||||||
|
Oklahoma Baptist University,Yes,758,681,484,35,59,1707,705,5390,3140,515,1290,63,71,15.1,18,5511,50
|
||||||
|
Oklahoma Christian University,Yes,776,765,351,22,44,1419,228,6400,3150,500,1900,58,64,16.2,8,6578,45
|
||||||
|
Oklahoma State University,No,4522,3913,2181,29,57,12830,1658,5336,3344,800,3100,84,92,15.3,14,6433,48
|
||||||
|
Otterbein College,Yes,1496,1205,428,26,57,1648,936,12888,4440,420,840,62,68,13.9,30,8802,87
|
||||||
|
Ouachita Baptist University,Yes,910,773,450,31,73,1310,61,6530,2800,500,1500,63,67,13.3,10,6413,65
|
||||||
|
Our Lady of the Lake University,Yes,2308,1336,295,22,46,1202,942,8530,3644,616,1576,56,64,14.9,25,7114,37
|
||||||
|
Pace University,Yes,8256,3750,1522,37,70,5809,4379,11000,5160,660,1115,90,95,13.8,10,10059,62
|
||||||
|
Pacific Lutheran University,Yes,1603,1392,504,31,68,2580,302,13312,4488,600,1516,78,78,11,23,9431,83
|
||||||
|
Pacific Union College,Yes,940,668,385,20,48,1316,139,11925,3825,630,1926,48,87,12.3,12,9157,69
|
||||||
|
Pacific University,Yes,943,849,288,41,71,1041,35,14210,3994,450,1100,76,76,10.9,22,11216,42
|
||||||
|
Pembroke State University,No,944,774,440,14,34,2174,529,6360,2760,550,1498,77,77,15,5,6443,48
|
||||||
|
Pennsylvania State Univ. Main Campus,No,19315,10344,3450,48,93,28938,2025,10645,4060,512,2394,77,96,18.1,19,8992,63
|
||||||
|
Pepperdine University,Yes,3821,2037,680,86,96,2488,625,18200,6770,500,700,95,98,11.6,13,16185,66
|
||||||
|
Peru State College,No,701,501,458,10,40,959,457,2580,2624,500,900,48,100,20.1,24,4870,44
|
||||||
|
Pfeiffer College,Yes,838,651,159,11,25,654,162,8640,3700,400,1915,62,62,12.2,13,7634,48
|
||||||
|
Philadelphia Coll. of Textiles and Sci.,Yes,1538,1259,468,19,42,1664,1042,11690,5062,600,1664,48,80,12.9,15,8028,68
|
||||||
|
Phillips University,Yes,692,576,174,19,50,597,83,10500,3860,600,940,58,64,11.6,19,8990,39
|
||||||
|
Piedmont College,Yes,663,562,127,20,40,641,63,5640,3620,600,750,89,89,13.2,17,7309,31
|
||||||
|
Pikeville College,Yes,404,400,169,28,48,797,100,6000,3000,500,500,48,57,13.4,14,5557,61
|
||||||
|
Pitzer College,Yes,1133,630,220,37,73,750,30,17688,5900,650,850,100,100,10.4,11,14820,73
|
||||||
|
Point Loma Nazarene College,Yes,809,687,428,20,43,1889,217,10178,4190,800,750,71,71,16.1,19,7895,54
|
||||||
|
Point Park College,Yes,875,744,207,7,38,1173,1402,9700,4830,400,1200,45,90,14.5,10,7652,66
|
||||||
|
Polytechnic University,Yes,1132,847,302,58,89,1379,214,16200,4200,436,2486,90,90,10.4,14,14329,62
|
||||||
|
Prairie View A. and M. University,No,2405,2234,1061,10,22,4564,448,4290,3500,598,1582,55,93,19.4,1,5967,35
|
||||||
|
Presbyterian College,Yes,1082,832,302,34,63,1133,30,11859,3635,554,1429,80,85,13.4,42,8354,85
|
||||||
|
Princeton University,Yes,13218,2042,1153,90,98,4540,146,19900,5910,675,1575,91,96,8.4,54,28320,99
|
||||||
|
Providence College,Yes,5139,3346,973,20,55,3717,1358,14400,6200,450,1100,66,74,18.4,35,8135,96
|
||||||
|
Purdue University at West Lafayette,No,21804,18744,5874,29,60,26213,4065,9556,3990,570,1060,86,86,18.2,15,8604,67
|
||||||
|
Queens College,Yes,516,392,154,32,62,630,549,11020,4970,610,1900,73,75,14,36,9315,58
|
||||||
|
Quincy University,Yes,1025,707,297,22,66,1070,72,10100,4140,450,1080,69,71,16.3,32,6880,80
|
||||||
|
Quinnipiac College,Yes,3712,2153,806,17,45,2677,714,12030,6140,1000,500,63,73,12,33,8847,86
|
||||||
|
Radford University,No,5702,4894,1742,15,37,8077,472,6684,4110,500,900,73,83,19.6,9,4519,62
|
||||||
|
Ramapo College of New Jersey,No,2088,957,362,6,29,2745,1938,4449,4860,600,1655,74,95,17.8,8,7333,47
|
||||||
|
Randolph-Macon College,Yes,1771,1325,306,21,46,1071,27,13840,3735,400,900,77,80,10.7,38,11080,74
|
||||||
|
Randolph-Macon Woman's College,Yes,696,616,169,35,66,653,56,13970,6110,370,920,88,97,9.2,24,16358,68
|
||||||
|
Reed College,Yes,1966,1436,327,47,80,1199,61,19960,5490,500,450,90,90,11.8,37,15886,68
|
||||||
|
Regis College,Yes,427,385,143,18,38,581,533,12700,5800,450,700,81,85,10.3,37,11758,84
|
||||||
|
Rensselaer Polytechnic Institute,Yes,4996,4165,936,53,82,4291,16,17475,5976,1230,1100,94,98,15.4,21,15605,70
|
||||||
|
Rhodes College,Yes,2302,1831,391,58,82,1345,59,15200,4768,550,1500,90,96,10.8,47,13388,77
|
||||||
|
Rider University,Yes,3586,2424,730,16,31,2748,1309,13250,5420,700,3100,84,92,12.3,23,11299,70
|
||||||
|
Ripon College,Yes,587,501,211,28,52,735,28,15200,4100,350,650,87,90,9.4,49,12472,64
|
||||||
|
Rivier College,Yes,484,386,141,6,28,590,1196,9870,4860,600,1100,59,59,12.2,19,6744,81
|
||||||
|
Roanoke College,Yes,2227,1790,437,27,54,1460,239,13425,4425,450,1200,85,89,13,26,9405,72
|
||||||
|
Rockhurst College,Yes,935,858,345,22,50,1127,754,9490,4100,500,1500,60,79,10.7,21,7519,79
|
||||||
|
Rocky Mountain College,Yes,560,392,270,11,31,743,118,8734,3362,600,625,56,78,11.3,27,6422,68
|
||||||
|
Roger Williams University,Yes,3304,2804,679,10,20,2111,1489,12520,6050,500,730,44,54,16.4,8,7957,61
|
||||||
|
Rollins College,Yes,1777,1151,382,31,55,1668,1052,16425,5220,955,750,81,85,13.3,23,11561,90
|
||||||
|
Rosary College,Yes,434,321,141,28,53,624,269,10950,4600,550,950,79,82,12.9,30,9264,81
|
||||||
|
Rowan College of New Jersey,No,3820,1431,695,21,70,5303,3942,4356,4830,800,800,76,81,22.1,6,7252,51
|
||||||
|
Rutgers at New Brunswick,No,48094,26330,4520,36,79,21401,3712,7410,4748,690,2009,90,95,19.5,19,10474,77
|
||||||
|
Rutgers State University at Camden,No,3366,1752,232,27,79,2585,1300,7411,4748,690,2009,90,95,18.6,12,10134,57
|
||||||
|
Rutgers State University at Newark,No,5785,2690,499,26,62,4005,1886,7410,4748,690,2009,90,95,17.4,16,11878,58
|
||||||
|
Sacred Heart University,Yes,2307,1896,509,19,51,1707,1889,11070,5780,400,600,71,73,14.8,16,7120,82
|
||||||
|
Saint Ambrose University,Yes,897,718,276,12,48,1345,390,10450,4020,500,1500,56,56,14.1,16,7444,70
|
||||||
|
Saint Anselm College,Yes,2095,1553,514,15,40,1873,94,12950,5400,450,1120,70,82,14.5,29,6719,97
|
||||||
|
Saint Cloud State University,No,3971,3306,1921,10,34,11493,2206,4259,2625,350,1884,70,75,18.9,10,4629,58
|
||||||
|
Saint Francis College IN,Yes,213,166,85,13,36,513,247,8670,3820,450,1000,43,78,12.5,4,7440,48
|
||||||
|
Saint Francis College,Yes,1046,824,284,21,45,1223,451,10880,5050,400,1235,64,64,19.3,24,7344,69
|
||||||
|
Saint John's University,Yes,933,800,444,18,45,1691,72,12247,4081,500,600,76,85,12,38,9853,70
|
||||||
|
Saint Joseph's College IN,Yes,920,684,225,24,42,815,222,11200,4250,600,950,55,60,14.8,19,7360,67
|
||||||
|
Saint Joseph's College,Yes,833,682,217,12,33,716,2196,9985,5180,500,800,53,89,27.2,8,4322,85
|
||||||
|
Saint Joseph's University,Yes,2519,2003,776,39,71,2473,1314,12750,6350,350,1690,84,90,17.4,13,8243,83
|
||||||
|
Saint Joseph College,Yes,292,241,96,20,52,543,712,12200,4600,650,950,87,90,11.2,32,8680,76
|
||||||
|
Saint Louis University,Yes,3294,2855,956,44,67,4576,1140,11690,4730,800,6800,84,94,4.6,19,18367,67
|
||||||
|
Saint Mary's College,Yes,888,734,393,26,60,1433,27,12730,4514,500,1525,74,95,9.9,31,11165,98
|
||||||
|
Saint Mary's College of Minnesota,Yes,876,802,367,14,35,1263,118,10800,3600,400,820,68,74,18.8,19,5081,78
|
||||||
|
Saint Mary-of-the-Woods College,Yes,150,130,88,23,50,341,768,10300,4130,500,1700,44,58,10.2,37,9678,75
|
||||||
|
Saint Michael's College,Yes,1910,1380,463,16,64,1715,106,13030,5860,500,750,79,88,14.5,34,10190,84
|
||||||
|
Saint Olaf College,Yes,2248,1673,745,38,73,2888,105,14350,3750,550,550,82,88,10,31,12502,83
|
||||||
|
Saint Peter's College,Yes,1606,1413,530,23,38,1921,1154,9408,5520,500,450,78,78,12.1,22,7669,53
|
||||||
|
Saint Vincent College,Yes,700,595,278,19,35,1035,182,10850,3936,500,900,62,64,12.3,31,8534,88
|
||||||
|
Saint Xavier University,Yes,785,647,295,15,65,1670,726,10860,4624,600,794,87,100,13.7,15,8953,55
|
||||||
|
Salem-Teikyo University,Yes,489,384,120,23,52,700,45,10575,3952,400,620,46,24,13,9,8946,98
|
||||||
|
Salem College,Yes,335,284,132,28,69,534,216,10475,6300,500,2000,68,68,11.2,46,9599,60
|
||||||
|
Salisbury State University,No,4216,2290,736,20,52,4296,1027,5130,4690,600,1450,73,75,17.9,18,5125,56
|
||||||
|
Samford University,Yes,1680,1395,691,34,76,2959,402,8236,3700,569,1650,74,75,14.7,17,9533,61
|
||||||
|
San Diego State University,No,9402,7020,2151,20,70,16407,5550,8384,5110,612,2400,87,93,19.5,7,7930,41
|
||||||
|
Santa Clara University,Yes,4019,2779,888,40,73,3891,128,13584,5928,630,1278,88,92,13.9,19,10872,100
|
||||||
|
Sarah Lawrence College,Yes,1380,768,263,57,82,1000,105,19300,6694,600,700,89,93,6.1,18,14779,83
|
||||||
|
Savannah Coll. of Art and Design,Yes,1109,688,386,20,65,1897,208,8325,5000,1200,1600,14,98,16.1,26,6874,55
|
||||||
|
Schreiner College,Yes,584,413,131,19,51,521,99,8955,5900,500,1488,51,56,11.8,23,8545,52
|
||||||
|
Scripps College,Yes,855,632,139,60,83,569,7,17238,7350,600,800,95,100,8.2,41,18372,73
|
||||||
|
Seattle Pacific University,Yes,1183,1016,411,42,82,1922,704,12669,4875,600,1250,83,85,16.8,20,10368,66
|
||||||
|
Seattle University,Yes,2115,1540,494,28,72,2993,347,12825,4375,500,1500,85,85,12.2,16,10175,89
|
||||||
|
Seton Hall University,Yes,4576,3565,1000,16,36,4384,1530,12000,6484,650,1000,81,84,14.4,15,10080,64
|
||||||
|
Seton Hill College,Yes,936,794,197,24,56,752,210,11240,4180,350,2000,71,71,11.2,37,10065,71
|
||||||
|
Shippensburg University of Penn.,No,5818,3281,1116,14,53,5268,300,7844,3504,450,1700,80,83,18.8,13,6719,72
|
||||||
|
Shorter College,Yes,540,445,165,23,70,1115,111,7210,3600,500,2000,62,65,13.2,18,7356,58
|
||||||
|
Siena College,Yes,2961,1932,628,24,68,2669,616,10800,5100,575,1090,71,82,14.1,42,8189,100
|
||||||
|
Siena Heights College,Yes,464,419,183,10,31,686,287,9240,3880,475,1090,29,49,7.2,17,9431,47
|
||||||
|
Simmons College,Yes,1003,782,295,23,53,1144,160,16160,6950,500,1200,74,81,8.9,33,14086,79
|
||||||
|
Simpson College,Yes,1016,872,300,27,57,1116,602,11250,4980,550,1400,66,73,15.8,36,7411,70
|
||||||
|
Sioux Falls College,Yes,437,400,211,13,35,614,271,8990,3064,500,1700,73,73,14.8,7,7881,48
|
||||||
|
Skidmore College,Yes,4293,2728,591,25,62,2322,263,18710,5970,500,700,87,92,12.7,29,14837,81
|
||||||
|
Smith College,Yes,2925,1598,632,51,88,2479,95,18820,6390,500,1050,85,97,10.3,44,21199,90
|
||||||
|
South Dakota State University,No,2807,2589,1701,13,37,7000,1103,3811,2190,500,1970,62,65,15,29,5084,67
|
||||||
|
Southeast Missouri State University,No,2281,1870,1408,18,43,6553,1246,4680,3540,200,2150,75,76,17.1,8,5916,45
|
||||||
|
Southeastern Oklahoma State Univ.,No,818,700,447,20,50,2962,651,3738,2619,450,1022,55,59,19.6,9,4444,53
|
||||||
|
Southern California College,Yes,385,340,193,18,38,784,127,9520,4124,630,1818,63,65,18.6,11,8219,43
|
||||||
|
Southern Illinois University at Edwardsville,No,2540,2195,994,13,40,6063,2550,5472,3598,221,2216,76,81,16.5,8,7498,43
|
||||||
|
Southern Methodist University,Yes,4301,3455,1166,41,69,4892,387,12772,5078,576,1802,74,88,13.5,17,12726,72
|
||||||
|
Southwest Baptist University,Yes,1093,1093,642,12,32,1770,967,7070,2500,400,1000,52,54,15.9,13,4718,71
|
||||||
|
Southwest Missouri State University,No,6118,5254,3204,15,37,13131,3374,4740,2590,500,1360,70,75,19.9,11,4632,56
|
||||||
|
Southwest State University,No,1047,938,511,13,33,2091,546,4285,2750,600,1800,58,75,16.5,31,6591,51
|
||||||
|
Southwestern Adventist College,Yes,321,318,172,11,27,620,280,7536,3736,430,1651,44,77,13,12,5309,36
|
||||||
|
Southwestern College,Yes,213,155,75,28,66,504,147,7200,3532,550,1500,56,56,11.8,12,7818,52
|
||||||
|
Southwestern University,Yes,1244,912,352,44,77,1177,43,11850,4675,600,1050,83,89,11.3,35,12995,67
|
||||||
|
Spalding University,Yes,283,201,97,10,45,589,263,8400,2800,600,900,50,56,10.6,40,6860,89
|
||||||
|
Spelman College,Yes,3713,1237,443,47,83,1971,107,7000,5565,660,2400,73,80,12.5,18,9988,65
|
||||||
|
Spring Arbor College,Yes,372,362,181,15,32,1501,353,8600,3550,385,665,48,48,15.4,9,10938,49
|
||||||
|
St. Bonaventure University,Yes,1489,1313,375,13,45,1688,131,10456,4927,500,1050,91,91,17.7,32,9828,78
|
||||||
|
St. John's College,Yes,323,278,122,31,51,393,4,16150,5450,275,800,63,72,7.2,26,15622,64
|
||||||
|
St. John Fisher College,Yes,1368,1064,354,19,51,1687,677,10570,5600,400,800,86,81,14.5,29,7908,66
|
||||||
|
St. Lawrence University,Yes,2753,1820,505,31,56,1801,45,18720,5730,650,825,90,94,11.5,38,14980,85
|
||||||
|
St. Martin's College,Yes,191,165,63,5,25,494,574,11550,4270,300,500,43,77,14.5,8,9209,40
|
||||||
|
St. Mary's College of California,Yes,2643,1611,465,36,80,2615,248,13332,6354,630,1584,88,89,16.1,17,9619,78
|
||||||
|
St. Mary's College of Maryland,No,1340,695,285,42,73,1315,209,6800,4730,675,1250,84,89,11.6,23,10357,63
|
||||||
|
St. Mary's University of San Antonio,Yes,1243,1020,414,33,60,2149,418,8678,3858,700,1736,82,83,16.2,7,7651,72
|
||||||
|
St. Norbert College,Yes,1334,1243,568,30,56,1946,95,12140,4450,425,1100,74,78,15.1,36,8595,88
|
||||||
|
St. Paul's College,Yes,651,581,243,8,17,617,34,5000,3650,600,600,45,45,14,8,8426,45
|
||||||
|
St. Thomas Aquinas College,Yes,861,609,215,10,27,1117,815,8650,5700,500,1750,69,73,16.1,13,6534,67
|
||||||
|
Stephens College,Yes,450,405,194,17,34,614,388,13900,5200,450,2150,46,63,10.9,17,9995,59
|
||||||
|
Stetson University,Yes,1557,1227,489,37,69,1964,81,12315,4565,600,1365,85,90,12.5,24,10307,73
|
||||||
|
Stevens Institute of Technology,Yes,1768,1249,380,51,93,1263,11,16900,5680,450,750,89,89,19,33,12837,79
|
||||||
|
Stockton College of New Jersey,No,4019,1579,710,23,65,4365,765,3040,4351,711,1125,78,92,19.5,7,5599,64
|
||||||
|
Stonehill College,Yes,3646,2300,585,25,69,2022,926,12170,6172,480,800,79,79,13,30,7495,97
|
||||||
|
SUNY at Albany,No,13528,9198,1843,16,61,10168,1231,6550,4355,700,1560,93,96,17.4,16,9075,74
|
||||||
|
SUNY at Binghamton,No,14463,6166,1757,60,94,8544,671,6550,4598,700,1000,83,100,18,15,8055,80
|
||||||
|
SUNY at Buffalo,No,15039,9649,3087,36,100,13963,3124,6550,4731,708,957,90,97,13.6,15,11177,56
|
||||||
|
SUNY at Stony Brook,No,12512,6969,1724,27,66,9744,1351,6550,4712,600,1200,91,96,10.5,7,13705,57
|
||||||
|
SUNY College at Brockport,No,7294,3564,904,7,34,5758,1363,6550,4460,500,705,79,83,19,14,6632,49
|
||||||
|
SUNY College at Oswego,No,8000,4556,1464,17,70,6943,869,6550,4810,500,1500,69,85,22,21,5280,63
|
||||||
|
SUNY College at Buffalo,No,5318,3515,1025,8,29,7626,2091,6550,4040,550,1230,71,78,18.7,12,7511,42
|
||||||
|
SUNY College at Cortland,No,7888,3519,1036,6,40,5011,346,6550,4680,630,1274,82,85,17.8,17,5563,53
|
||||||
|
SUNY College at Fredonia,No,4877,2798,814,13,48,4123,298,6550,4420,620,1481,82,90,16.3,10,6442,66
|
||||||
|
SUNY College at Geneseo,No,8598,4562,1143,56,93,5060,146,6550,4170,600,650,79,84,19.1,25,5716,76
|
||||||
|
SUNY College at New Paltz,No,8399,3609,656,19,53,4658,1478,6550,4240,550,1500,85,93,15.3,8,6608,53
|
||||||
|
SUNY College at Plattsburgh,No,5549,3583,853,9,40,5004,475,6550,4176,600,1380,80,90,17.9,16,6174,65
|
||||||
|
SUNY College at Potsdam,No,3150,2289,650,16,51,3598,234,6840,4660,500,1000,71,75,15.1,17,6436,59
|
||||||
|
SUNY College at Purchase,No,2119,1264,390,5,33,2478,1441,6550,4760,1125,1362,80,100,14.9,8,8170,46
|
||||||
|
Susquehanna University,Yes,2096,1512,465,27,59,1442,166,16130,4710,400,800,83,86,13.9,37,10554,90
|
||||||
|
Sweet Briar College,Yes,462,402,146,36,68,527,41,14500,6000,500,600,91,99,6.5,48,18953,61
|
||||||
|
Syracuse University,Yes,10477,7260,2442,28,67,10142,117,15150,6870,635,960,73,84,11.3,13,14231,67
|
||||||
|
Tabor College,Yes,257,183,109,19,41,396,38,7850,3410,400,1500,55,70,10,15,7233,53
|
||||||
|
Talladega College,Yes,4414,1500,335,30,60,908,119,5666,2964,1000,1400,56,58,15.5,7,5970,46
|
||||||
|
Taylor University,Yes,1769,1092,437,41,80,1757,81,10965,4000,450,1250,60,61,14.2,32,8294,98
|
||||||
|
Tennessee Wesleyan College,Yes,232,182,99,7,29,402,237,7070,3640,400,3158,59,65,8.9,16,6286,36
|
||||||
|
Texas A&M Univ. at College Station,No,14474,10519,6392,49,85,31643,2798,5130,3412,600,2144,89,91,23.1,29,8471,69
|
||||||
|
Texas A&M University at Galveston,No,529,481,243,22,47,1206,134,4860,3122,600,650,103,88,17.4,16,6415,43
|
||||||
|
Texas Christian University,Yes,4095,3079,1195,33,64,5064,660,8490,3320,650,2400,81,93,14.8,23,9158,64
|
||||||
|
Texas Lutheran College,Yes,497,423,215,27,57,895,429,7850,3410,490,1700,54,58,13.8,24,7002,50
|
||||||
|
Texas Southern University,No,4345,3245,2604,15,85,5584,3101,7860,3360,600,1700,65,75,18.2,21,3605,10
|
||||||
|
Texas Wesleyan University,Yes,592,501,279,19,44,1204,392,6400,3484,600,1800,80,83,14.5,10,7936,43
|
||||||
|
The Citadel,No,1500,1242,611,12,36,2024,292,7070,2439,400,779,95,94,17.1,17,7744,84
|
||||||
|
Thiel College,Yes,1154,951,253,15,31,791,140,11172,4958,700,1350,68,76,11.6,16,9186,60
|
||||||
|
Tiffin University,Yes,845,734,254,5,21,662,351,7600,3800,600,1200,59,74,19,40,5096,39
|
||||||
|
Transylvania University,Yes,759,729,244,57,81,867,51,10900,4450,500,1000,81,91,12.1,41,10219,70
|
||||||
|
Trenton State College,No,5042,2312,944,55,94,5167,902,5391,5411,700,1000,81,87,14.4,6,8504,81
|
||||||
|
Tri-State University,Yes,1262,1102,276,14,40,978,98,9456,4350,468,1323,53,53,12.8,24,7603,65
|
||||||
|
Trinity College CT,Yes,3058,1798,478,46,84,1737,244,18810,5690,500,680,91,96,10.4,48,18034,91
|
||||||
|
Trinity College DC,Yes,247,189,100,19,49,309,639,11412,6430,500,900,89,93,8.3,37,11806,96
|
||||||
|
Trinity College VT,Yes,222,185,91,16,41,484,541,11010,5208,550,500,58,78,10.4,26,9586,78
|
||||||
|
Trinity University,Yes,2425,1818,601,62,93,2110,95,12240,5150,500,490,94,96,9.6,20,14703,93
|
||||||
|
Tulane University,Yes,7033,5125,1223,47,75,4941,1534,19040,5950,350,800,98,98,9.1,21,16920,74
|
||||||
|
Tusculum College,Yes,626,372,145,12,34,983,40,7700,3400,450,800,70,70,21.9,28,4933,52
|
||||||
|
Tuskegee University,Yes,2267,1827,611,20,59,2825,144,6735,3395,600,1425,70,74,12.2,7,10872,65
|
||||||
|
Union College KY,Yes,484,384,177,9,45,634,78,7800,2950,500,600,60,88,14.1,9,6864,64
|
||||||
|
Union College NY,Yes,3495,1712,528,49,84,1915,123,18732,6204,450,1024,94,96,11.5,49,15411,88
|
||||||
|
Univ. of Wisconsin at OshKosh,No,4800,2900,1515,14,48,7764,1472,6874,2394,518,1890,73,78,19.2,14,5901,56
|
||||||
|
University of Alabama at Birmingham,No,1797,1260,938,24,35,6960,4698,4440,5175,750,2200,96,96,6.7,16,16352,33
|
||||||
|
University of Arkansas at Fayetteville,No,3235,3108,2133,25,65,9978,1530,5028,3300,500,2000,73,89,14.8,10,6820,39
|
||||||
|
University of California at Berkeley,No,19873,8252,3215,95,100,19532,2061,11648,6246,636,1933,93,97,15.8,10,13919,78
|
||||||
|
University of California at Irvine,No,15698,10775,2478,85,100,12677,864,12024,5302,790,1818,96,96,16.1,11,15934,66
|
||||||
|
University of Central Florida,No,6986,2959,1918,25,60,12330,7152,6618,4234,700,1600,80,98,22.2,9,6742,46
|
||||||
|
University of Charleston,Yes,682,535,204,22,43,771,611,9500,3540,400,750,26,58,2.5,10,7683,57
|
||||||
|
University of Chicago,Yes,6348,2999,922,68,94,3340,39,18930,6380,500,1254,99,99,5.3,36,36854,90
|
||||||
|
University of Cincinnati,No,6855,5553,2408,26,57,11036,2011,8907,4697,556,1851,89,95,10.8,6,13889,54
|
||||||
|
University of Connecticut at Storrs,No,9735,7187,2064,23,63,12478,1660,11656,5072,700,2300,89,95,16,16,10178,71
|
||||||
|
University of Dallas,Yes,681,588,246,44,74,1058,73,10760,6230,500,1200,85,93,13.4,26,8731,63
|
||||||
|
University of Dayton,Yes,6361,5293,1507,26,51,5889,665,11380,4220,500,900,81,85,14.8,25,8894,93
|
||||||
|
University of Delaware,Yes,14446,10516,3252,22,57,14130,4522,10220,4230,530,1300,82,87,18.3,15,10650,75
|
||||||
|
University of Denver,Yes,2974,2001,580,29,60,2666,554,15192,4695,400,1350,84,91,15.9,21,11762,67
|
||||||
|
University of Detroit Mercy,Yes,927,731,415,24,50,2149,2217,11130,3996,600,2166,72,79,13.5,14,10891,51
|
||||||
|
University of Dubuque,Yes,576,558,137,11,39,662,131,10430,3620,400,1500,85,98,16.5,18,8767,45
|
||||||
|
University of Evansville,Yes,2096,1626,694,35,67,2551,407,11800,4340,700,960,60,81,15.8,26,7780,77
|
||||||
|
University of Florida,No,12445,8836,3623,54,85,24470,3286,7090,4180,630,1530,88,97,13.4,20,14737,66
|
||||||
|
University of Georgia,No,11220,7871,3320,43,79,19553,2748,5697,3600,525,1755,88,95,14.7,22,7881,63
|
||||||
|
University of Hartford,Yes,5081,4040,1194,11,26,3768,1415,14220,6000,500,1440,61,76,10.7,9,10625,66
|
||||||
|
University of Hawaii at Manoa,No,3580,2603,1627,36,69,11028,2411,4460,3038,687,1281,85,87,11.8,6,12833,54
|
||||||
|
University of Illinois - Urbana,No,14939,11652,5705,52,88,25422,911,7560,4574,500,1982,87,90,17.4,13,8559,81
|
||||||
|
University of Illinois at Chicago,No,8384,5727,2710,22,50,13518,2916,7230,5088,630,3228,82,84,10,6,13883,34
|
||||||
|
University of Indianapolis,Yes,1487,1276,388,26,51,1417,1646,11120,4080,525,1405,55,56,11.1,23,6735,69
|
||||||
|
University of Kansas,No,8579,5561,3681,25,50,17880,1673,6994,3384,700,2681,88,94,13.7,17,9657,57
|
||||||
|
University of La Verne,Yes,1597,969,226,16,38,1431,1522,13540,5050,630,2298,66,68,14.1,23,10139,47
|
||||||
|
University of Louisville,No,4777,3057,1823,16,33,9844,6198,6540,3600,530,2440,84,92,11.1,24,10207,31
|
||||||
|
University of Maine at Farmington,No,1208,803,438,20,48,1906,344,6810,3970,450,1647,67,75,15.9,26,5712,59
|
||||||
|
University of Maine at Machias,No,441,369,172,17,45,633,317,6600,3680,600,400,46,46,15.1,4,5935,64
|
||||||
|
University of Maine at Presque Isle,No,461,381,235,10,40,974,503,6600,3630,400,1675,67,67,15.2,11,6408,35
|
||||||
|
University of Maryland at Baltimore County,No,4269,2594,985,27,57,6476,2592,8594,4408,494,2768,82,88,18.4,6,7618,55
|
||||||
|
University of Maryland at College Park,No,14292,10315,3409,22,53,19340,3991,8723,5146,550,1550,89,92,18.1,12,9021,63
|
||||||
|
University of Massachusetts at Amherst,No,14438,12414,3816,12,39,16282,1940,8566,3897,500,1400,88,92,16.7,15,10276,68
|
||||||
|
University of Massachusetts at Dartmouth,No,3347,2597,1006,10,37,4664,1630,6919,4500,500,1250,74,90,15,20,7462,56
|
||||||
|
University of Miami,Yes,7122,5386,1643,42,69,7760,876,16500,6526,630,1985,82,94,5.9,17,17500,59
|
||||||
|
University of Michigan at Ann Arbor,No,19152,12940,4893,66,92,22045,1339,15732,4659,476,1600,90,98,11.5,26,14847,87
|
||||||
|
University of Minnesota at Duluth,No,4192,3126,1656,15,45,5887,1254,8828,3474,753,2610,79,91,19,11,6393,53
|
||||||
|
University of Minnesota at Morris,No,1458,874,588,56,86,1846,154,9843,3180,600,1500,74,78,14.6,16,6716,51
|
||||||
|
University of Minnesota Twin Cities,No,11054,6397,3524,26,55,16502,21836,8949,3744,714,2910,88,90,12.2,37,16122,45
|
||||||
|
University of Mississippi,No,3844,3383,1669,26,47,7524,804,4916,3810,600,550,81,86,20.3,14,6971,53
|
||||||
|
University of Missouri at Columbia,No,6574,4637,2940,32,62,14782,1583,9057,3485,600,1983,87,87,12.7,15,10145,58
|
||||||
|
University of Missouri at Rolla,No,1877,1826,823,49,77,3926,561,9057,3600,700,1435,88,88,14.4,23,9699,49
|
||||||
|
University of Missouri at Saint Louis,No,1618,1141,479,18,54,4793,4552,7246,3964,500,4288,71,73,13.4,15,6433,48
|
||||||
|
University of Mobile,Yes,452,331,269,17,54,1417,301,6150,3680,550,1200,59,63,16.6,4,5412,52
|
||||||
|
University of Montevallo,No,1351,892,570,18,78,2385,331,4440,3030,300,600,72,72,18.9,8,5883,51
|
||||||
|
University of Nebraska at Lincoln,No,6277,6003,3526,33,63,16454,3171,5595,3145,500,2070,86,92,15.1,48,6813,53
|
||||||
|
University of New England,Yes,1209,750,265,19,54,820,159,11450,5045,900,2500,72,75,11.4,13,9718,64
|
||||||
|
University of New Hampshire,No,9750,7640,2529,24,62,10358,1338,11180,3862,650,2450,89,87,17.5,16,7855,75
|
||||||
|
University of North Carolina at Asheville,No,1757,979,394,32,74,2033,1078,5972,3420,600,750,77,83,13,11,7011,37
|
||||||
|
University of North Carolina at Chapel Hill,No,14596,5985,3331,75,92,14609,1100,8400,4200,550,1200,88,93,8.9,23,15893,83
|
||||||
|
University of North Carolina at Charlotte,No,5803,4441,1730,19,62,10099,3255,7248,3109,600,1900,79,91,16.8,7,6227,62
|
||||||
|
University of North Carolina at Greensboro,No,5191,4134,1500,15,44,7532,1847,8677,3505,600,1300,75,94,15.5,17,7392,53
|
||||||
|
University of North Carolina at Wilmington,No,6071,3856,1449,15,67,6635,1145,7558,3680,500,1500,82,85,19.1,15,6005,55
|
||||||
|
University of North Dakota,No,2777,2249,1652,20,54,8334,1435,5634,2703,450,1200,97,97,15.9,16,9424,49
|
||||||
|
University of North Florida,No,1800,1253,560,44,85,3876,3588,6634,4360,600,2604,82,85,17.8,14,6104,47
|
||||||
|
University of North Texas,No,4418,2737,2049,23,51,14047,5134,4104,3579,450,1700,86,94,22.6,6,5657,35
|
||||||
|
University of Northern Colorado,No,5530,4007,1697,12,37,8463,1498,7731,4128,540,2286,75,75,21.5,8,6309,40
|
||||||
|
University of Northern Iowa,No,4144,3379,1853,18,52,10135,1448,6197,2930,595,2380,78,82,16.3,26,6333,77
|
||||||
|
University of Notre Dame,Yes,7700,3700,1906,79,96,7671,30,16850,4400,600,1350,96,92,13.1,46,13936,97
|
||||||
|
University of Oklahoma,No,4743,3970,2233,32,63,13436,2582,5173,3526,765,3176,86,90,11.5,11,10244,44
|
||||||
|
University of Oregon,No,8631,6732,2546,25,61,11669,1605,10602,3660,570,1530,79,87,19.7,13,8020,54
|
||||||
|
University of Pennsylvania,Yes,12394,5232,2464,85,100,9205,531,17020,7270,500,1544,95,96,6.3,38,25765,93
|
||||||
|
University of Pittsburgh-Main Campus,No,8586,6383,2503,25,59,13138,4289,10786,4560,400,900,93,93,7.8,10,13789,66
|
||||||
|
University of Portland,Yes,1758,1485,419,27,58,2041,174,12040,4100,600,1100,92,96,13.2,17,9060,72
|
||||||
|
University of Puget Sound,Yes,4044,2826,688,51,83,2738,138,16230,4500,630,1800,79,86,15,17,11217,63
|
||||||
|
University of Rhode Island,No,9643,7751,1968,12,40,8894,2456,10330,5558,500,1250,84,89,16.6,7,9158,63
|
||||||
|
University of Richmond,Yes,5892,2718,756,46,72,2854,594,14500,3285,700,1125,75,89,11.7,32,11984,100
|
||||||
|
University of Rochester,Yes,8766,5498,1243,56,75,5071,438,17840,6582,500,882,93,99,5.9,23,26037,80
|
||||||
|
University of San Diego,Yes,3934,2735,886,40,70,3698,217,13600,5940,630,1820,93,96,15.6,13,10813,66
|
||||||
|
University of San Francisco,Yes,2306,1721,538,23,48,4309,549,13226,6452,750,2450,86,86,13.6,8,10074,62
|
||||||
|
University of Sci. and Arts of Oklahoma,No,285,280,208,21,43,1140,473,3687,1920,600,1800,67,77,23.6,3,3864,43
|
||||||
|
University of Scranton,Yes,4471,2942,910,29,60,3674,493,11584,5986,650,800,83,83,14.1,41,9131,92
|
||||||
|
University of South Carolina at Aiken,No,848,560,377,14,24,1855,1412,5800,3066,500,1500,62,62,14.8,3,5035,48
|
||||||
|
University of South Carolina at Columbia,No,7693,5815,2328,30,66,12594,3661,8074,3522,495,2941,84,88,16.9,18,8246,63
|
||||||
|
University of South Florida,No,7589,4676,1876,29,63,14770,10962,6760,3776,500,2180,84,89,17,7,11020,47
|
||||||
|
University of Southern California,Yes,12229,8498,2477,45,71,13259,1429,17230,6482,600,2210,90,94,11.4,10,17007,68
|
||||||
|
University of Southern Colorado,No,1401,1239,605,10,34,3716,675,7100,4380,540,2948,63,88,19.4,0,5389,36
|
||||||
|
University of Southern Indiana,No,2379,2133,1292,8,25,4283,2973,4973,3192,500,1425,56,65,22,21,4078,38
|
||||||
|
University of Southern Mississippi,No,2850,2044,1046,20,50,9260,1387,4652,2470,500,500,78,99,18.7,23,5917,45
|
||||||
|
University of St. Thomas MN,Yes,2057,1807,828,26,53,4106,982,11712,4037,500,800,80,80,13.8,13,8546,89
|
||||||
|
University of St. Thomas TX,Yes,374,280,185,45,77,995,408,8550,4050,500,1344,75,75,12.6,17,7237,62
|
||||||
|
University of Tennessee at Knoxville,No,7473,5372,3013,27,53,15749,3237,5764,3262,750,3300,86,92,16.5,22,8612,53
|
||||||
|
University of Texas at Arlington,No,3281,2559,1448,19,43,10975,8431,4422,2780,500,2850,73,73,21,4,4696,29
|
||||||
|
University of Texas at Austin,No,14752,9572,5329,48,85,30017,5189,5130,3309,650,3140,91,99,19.7,11,7837,65
|
||||||
|
University of Texas at San Antonio,No,4217,3100,1686,17,46,9375,5457,4104,5376,452,1200,94,100,25.3,3,4329,50
|
||||||
|
University of the Arts,Yes,974,704,290,5,22,1145,39,12520,3860,1300,700,16,59,7.5,9,11641,57
|
||||||
|
University of the Pacific,Yes,2459,1997,582,36,66,2664,299,16320,5326,646,1171,87,94,11.2,14,13706,65
|
||||||
|
University of the South,Yes,1445,966,326,46,83,1129,24,15350,4080,450,810,89,93,10.3,52,18784,82
|
||||||
|
University of Tulsa,Yes,1712,1557,696,41,68,2936,433,11750,4160,1200,2350,94,96,11.5,10,11743,47
|
||||||
|
University of Utah,No,5095,4491,2400,27,53,13894,8374,6857,3975,858,3093,89,93,12.8,9,9275,37
|
||||||
|
University of Vermont,No,7663,6008,1735,18,51,7353,1674,15516,4928,500,990,87,90,9.9,10,12646,79
|
||||||
|
University of Virginia,No,15849,5384,2678,74,95,11278,114,12212,3792,500,1000,90,92,9.5,22,13597,95
|
||||||
|
University of Washington,No,12749,7025,3343,40,81,20356,4582,8199,4218,708,2172,96,94,9,10,16527,65
|
||||||
|
University of West Florida,No,1558,1254,472,20,57,3754,2477,6172,3994,541,1387,83,87,23.4,12,8488,53
|
||||||
|
University of Wisconsin-Stout,No,2593,1966,1030,9,32,6038,579,6704,2592,376,1750,78,78,21,17,6254,65
|
||||||
|
University of Wisconsin-Superior,No,910,910,342,14,53,1434,417,7032,2780,550,1960,75,81,15.2,15,6490,36
|
||||||
|
University of Wisconsin-Whitewater,No,4400,3719,1472,12,38,7804,1552,6950,2500,300,1200,90,95,23.1,16,5559,67
|
||||||
|
University of Wisconsin at Green Bay,No,2409,1939,759,17,50,3819,1347,6900,2800,475,1200,81,89,22.2,1,5968,46
|
||||||
|
University of Wisconsin at Madison,No,14901,10932,4631,36,80,23945,2200,9096,4290,535,1545,93,96,11.5,20,11006,72
|
||||||
|
University of Wisconsin at Milwaukee,No,5244,3782,1930,12,37,11561,7443,8786,2964,570,1980,79,87,15.9,8,8094,38
|
||||||
|
University of Wyoming,No,2029,1516,1073,23,46,7535,1488,5988,3422,600,1500,91,94,15.1,13,8745,45
|
||||||
|
Upper Iowa University,Yes,663,452,192,10,35,1481,1160,8840,3060,500,1000,69,75,17.4,19,3733,78
|
||||||
|
Ursinus College,Yes,1399,1026,308,44,77,1131,17,14900,5160,500,800,82,85,11.6,40,12082,79
|
||||||
|
Ursuline College,Yes,325,260,86,21,47,699,717,9600,4202,450,750,39,69,10.5,15,7164,68
|
||||||
|
Valley City State University,No,368,344,212,5,27,863,189,4286,2570,600,2000,39,41,14.9,25,4958,40
|
||||||
|
Valparaiso University,Yes,2075,1727,520,49,81,2501,198,11800,3260,500,800,87,89,14.2,23,9681,95
|
||||||
|
Vanderbilt University,Yes,7791,4690,1499,71,92,5500,90,17865,6525,630,952,93,98,5.8,26,23850,83
|
||||||
|
Vassar College,Yes,3550,1877,653,53,87,2164,77,18920,5950,600,800,90,98,9.7,39,17089,90
|
||||||
|
Villanova University,Yes,7759,5588,1477,30,68,6362,1292,15925,6507,400,300,89,90,13.4,24,10458,96
|
||||||
|
Virginia Commonwealth University,No,4963,3497,1567,18,45,10262,5065,10217,4182,500,3630,81,87,8.7,11,11183,45
|
||||||
|
Virginia State University,No,2996,2440,704,2,30,3006,338,5587,4845,500,600,61,63,16,11,5733,31
|
||||||
|
Virginia Tech,No,15712,11719,4277,29,53,18511,604,10260,3176,740,2200,85,89,13.8,20,8944,73
|
||||||
|
Virginia Union University,Yes,1847,1610,453,19,59,1298,67,7384,3494,500,1763,51,67,13.7,8,6757,30
|
||||||
|
Virginia Wesleyan College,Yes,1470,900,287,20,49,1130,417,10900,5100,500,550,70,81,15.7,14,7804,68
|
||||||
|
Viterbo College,Yes,647,518,271,17,43,1014,387,9140,3365,500,2245,51,65,10.7,31,8050,73
|
||||||
|
Voorhees College,Yes,1465,1006,188,10,30,703,20,4450,2522,500,1200,43,43,22.9,3,5861,58
|
||||||
|
Wabash College,Yes,800,623,256,41,76,801,5,12925,4195,500,635,78,85,9.9,55,14904,72
|
||||||
|
Wagner College,Yes,1416,1015,417,10,44,1324,117,13500,5800,585,1700,67,78,13.2,23,9006,75
|
||||||
|
Wake Forest University,Yes,5661,2392,903,75,88,3499,172,13850,4360,500,1250,95,97,4.3,37,41766,89
|
||||||
|
Walsh University,Yes,1092,890,477,27,92,847,497,8670,4180,500,1450,42,58,11.3,33,5738,68
|
||||||
|
Warren Wilson College,Yes,440,311,112,25,49,466,7,10000,3052,400,1100,65,75,11.4,20,9430,63
|
||||||
|
Wartburg College,Yes,1231,1074,345,34,66,1295,105,11600,3610,400,850,66,91,12.4,37,7735,67
|
||||||
|
Washington and Jefferson College,Yes,1305,1100,334,42,64,1098,151,16260,4005,300,500,91,91,12.1,40,10162,86
|
||||||
|
Washington and Lee University,Yes,3315,1096,425,68,93,1584,3,13750,4619,680,1115,81,96,9.6,45,15736,90
|
||||||
|
Washington College,Yes,1209,942,214,31,60,822,46,15276,5318,500,300,79,86,11.2,37,10830,65
|
||||||
|
Washington State University,No,6540,5839,2440,31,70,14445,1344,8200,4210,800,2719,84,87,16.9,30,10912,56
|
||||||
|
Washington University,Yes,7654,5259,1254,62,93,4879,1274,18350,5775,768,1512,91,98,3.9,31,45702,90
|
||||||
|
Wayne State College,No,1373,1373,724,6,21,2754,474,2700,2660,540,1660,60,68,20.3,29,4550,52
|
||||||
|
Waynesburg College,Yes,1190,978,324,12,30,1280,61,8840,3620,500,1200,57,58,16.2,26,6563,63
|
||||||
|
Webber College,Yes,280,143,79,5,27,327,110,5590,2900,650,1952,53,63,15.1,4,4839,90
|
||||||
|
Webster University,Yes,665,462,226,17,44,1739,1550,9160,4340,500,500,68,68,20.6,14,6951,48
|
||||||
|
Wellesley College,Yes,2895,1249,579,80,96,2195,156,18345,5995,500,700,94,98,10.6,51,21409,91
|
||||||
|
Wells College,Yes,318,240,130,40,85,416,19,14900,5550,600,500,93,98,8.3,42,13935,69
|
||||||
|
Wentworth Institute of Technology,Yes,1480,1257,452,6,25,2961,572,9850,6050,850,920,10,68,15.4,8,17858,64
|
||||||
|
Wesley College,Yes,980,807,350,10,25,872,448,9890,4674,500,1350,52,57,14.4,15,6243,84
|
||||||
|
Wesleyan University,Yes,4772,1973,712,60,86,2714,27,19130,5600,1400,1400,90,94,12.1,39,16262,92
|
||||||
|
West Chester University of Penn.,No,6502,3539,1372,11,51,7484,1904,7844,4108,400,2000,76,79,15.3,16,6773,52
|
||||||
|
West Liberty State College,No,1164,1062,478,12,25,2138,227,4470,2890,600,1210,33,33,16.3,10,4249,60
|
||||||
|
West Virginia Wesleyan College,Yes,1566,1400,483,28,55,1509,170,14200,3775,450,1100,58,81,16.4,42,8080,67
|
||||||
|
Western Carolina University,No,3224,2519,1057,11,31,5000,706,6390,2380,110,1622,67,78,14.6,9,6554,55
|
||||||
|
Western Maryland College,Yes,1205,984,278,31,50,1071,98,14510,5340,500,1400,84,91,12.5,39,10026,60
|
||||||
|
Western Michigan University,No,9167,7191,2738,24,53,15739,4278,6940,4100,500,1700,80,84,24.7,11,5983,55
|
||||||
|
Western New England College,Yes,1650,1471,409,7,21,1803,1116,8994,5500,498,2065,74,97,15.4,15,8409,59
|
||||||
|
Western State College of Colorado,No,2702,1623,604,7,24,2315,146,5918,3755,500,2050,76,79,19.4,4,4599,52
|
||||||
|
Western Washington University,No,5548,3563,1549,30,71,8909,506,8124,4144,639,2385,83,89,22.7,10,7203,61
|
||||||
|
Westfield State College,No,3100,2150,825,3,20,3234,941,5542,3788,500,1300,75,79,15.7,20,4222,65
|
||||||
|
Westminster College MO,Yes,662,553,184,20,43,665,37,10720,4050,600,1650,66,70,12.5,20,7925,62
|
||||||
|
Westminster College,Yes,996,866,377,29,58,1411,72,12065,3615,430,685,62,78,12.5,41,8596,80
|
||||||
|
Westminster College of Salt Lake City,Yes,917,720,213,21,60,979,743,8820,4050,600,2025,68,83,10.5,34,7170,50
|
||||||
|
Westmont College,No,950,713,351,42,72,1276,9,14320,5304,490,1410,77,77,14.9,17,8837,87
|
||||||
|
Wheaton College IL,Yes,1432,920,548,56,84,2200,56,11480,4200,530,1400,81,83,12.7,40,11916,85
|
||||||
|
Westminster College PA,Yes,1738,1373,417,21,55,1335,30,18460,5970,700,850,92,96,13.2,41,22704,71
|
||||||
|
Wheeling Jesuit College,Yes,903,755,213,15,49,971,305,10500,4545,600,600,66,71,14.1,27,7494,72
|
||||||
|
Whitman College,Yes,1861,998,359,45,77,1220,46,16670,4900,750,800,80,83,10.5,51,13198,72
|
||||||
|
Whittier College,Yes,1681,1069,344,35,63,1235,30,16249,5699,500,1998,84,92,13.6,29,11778,52
|
||||||
|
Whitworth College,Yes,1121,926,372,43,70,1270,160,12660,4500,678,2424,80,80,16.9,20,8328,80
|
||||||
|
Widener University,Yes,2139,1492,502,24,64,2186,2171,12350,5370,500,1350,88,86,12.6,19,9603,63
|
||||||
|
Wilkes University,Yes,1631,1431,434,15,36,1803,603,11150,5130,550,1260,78,92,13.3,24,8543,67
|
||||||
|
Willamette University,Yes,1658,1327,395,49,80,1595,159,14800,4620,400,790,91,94,13.3,37,10779,68
|
||||||
|
William Jewell College,Yes,663,547,315,32,67,1279,75,10060,2970,500,2600,74,80,11.2,19,7885,59
|
||||||
|
William Woods University,Yes,469,435,227,17,39,851,120,10535,4365,550,3700,39,66,12.9,16,7438,52
|
||||||
|
Williams College,Yes,4186,1245,526,81,96,1988,29,19629,5790,500,1200,94,99,9,64,22014,99
|
||||||
|
Wilson College,Yes,167,130,46,16,50,199,676,11428,5084,450,475,67,76,8.3,43,10291,67
|
||||||
|
Wingate College,Yes,1239,1017,383,10,34,1207,157,7820,3400,550,1550,69,81,13.9,8,7264,91
|
||||||
|
Winona State University,No,3325,2047,1301,20,45,5800,872,4200,2700,300,1200,53,60,20.2,18,5318,58
|
||||||
|
Winthrop University,No,2320,1805,769,24,61,3395,670,6400,3392,580,2150,71,80,12.8,26,6729,59
|
||||||
|
Wisconsin Lutheran College,Yes,152,128,75,17,41,282,22,9100,3700,500,1400,48,48,8.5,26,8960,50
|
||||||
|
Wittenberg University,Yes,1979,1739,575,42,68,1980,144,15948,4404,400,800,82,95,12.8,29,10414,78
|
||||||
|
Wofford College,Yes,1501,935,273,51,83,1059,34,12680,4150,605,1440,91,92,15.3,42,7875,75
|
||||||
|
Worcester Polytechnic Institute,Yes,2768,2314,682,49,86,2802,86,15884,5370,530,730,92,94,15.2,34,10774,82
|
||||||
|
Worcester State College,No,2197,1515,543,4,26,3089,2029,6797,3900,500,1200,60,60,21,14,4469,40
|
||||||
|
Xavier University,Yes,1959,1805,695,24,47,2849,1107,11520,4960,600,1250,73,75,13.3,31,9189,83
|
||||||
|
Xavier University of Louisiana,Yes,2097,1915,695,34,61,2793,166,6900,4200,617,781,67,75,14.4,20,8323,49
|
||||||
|
Yale University,Yes,10705,2453,1317,95,99,5217,83,19840,6510,630,2115,96,96,5.8,49,40386,99
|
||||||
|
York College of Pennsylvania,Yes,2989,1855,691,28,63,2988,1726,4990,3560,500,1250,75,75,18.1,28,4509,99
|
||||||
|
401
datasets/Credit.csv
Normal file
401
datasets/Credit.csv
Normal file
@ -0,0 +1,401 @@
|
|||||||
|
,Income,Limit,Rating,Cards,Age,Education,Gender,Student,Married,Ethnicity,Balance
|
||||||
|
1,14.891,3606,283,2,34,11,Male,No,Yes,Caucasian,333
|
||||||
|
2,106.025,6645,483,3,82,15,Female,Yes,Yes,Asian,903
|
||||||
|
3,104.593,7075,514,4,71,11,Male,No,No,Asian,580
|
||||||
|
4,148.924,9504,681,3,36,11,Female,No,No,Asian,964
|
||||||
|
5,55.882,4897,357,2,68,16,Male,No,Yes,Caucasian,331
|
||||||
|
6,80.18,8047,569,4,77,10,Male,No,No,Caucasian,1151
|
||||||
|
7,20.996,3388,259,2,37,12,Female,No,No,African American,203
|
||||||
|
8,71.408,7114,512,2,87,9,Male,No,No,Asian,872
|
||||||
|
9,15.125,3300,266,5,66,13,Female,No,No,Caucasian,279
|
||||||
|
10,71.061,6819,491,3,41,19,Female,Yes,Yes,African American,1350
|
||||||
|
11,63.095,8117,589,4,30,14,Male,No,Yes,Caucasian,1407
|
||||||
|
12,15.045,1311,138,3,64,16,Male,No,No,Caucasian,0
|
||||||
|
13,80.616,5308,394,1,57,7,Female,No,Yes,Asian,204
|
||||||
|
14,43.682,6922,511,1,49,9,Male,No,Yes,Caucasian,1081
|
||||||
|
15,19.144,3291,269,2,75,13,Female,No,No,African American,148
|
||||||
|
16,20.089,2525,200,3,57,15,Female,No,Yes,African American,0
|
||||||
|
17,53.598,3714,286,3,73,17,Female,No,Yes,African American,0
|
||||||
|
18,36.496,4378,339,3,69,15,Female,No,Yes,Asian,368
|
||||||
|
19,49.57,6384,448,1,28,9,Female,No,Yes,Asian,891
|
||||||
|
20,42.079,6626,479,2,44,9,Male,No,No,Asian,1048
|
||||||
|
21,17.7,2860,235,4,63,16,Female,No,No,Asian,89
|
||||||
|
22,37.348,6378,458,1,72,17,Female,No,No,Caucasian,968
|
||||||
|
23,20.103,2631,213,3,61,10,Male,No,Yes,African American,0
|
||||||
|
24,64.027,5179,398,5,48,8,Male,No,Yes,African American,411
|
||||||
|
25,10.742,1757,156,3,57,15,Female,No,No,Caucasian,0
|
||||||
|
26,14.09,4323,326,5,25,16,Female,No,Yes,African American,671
|
||||||
|
27,42.471,3625,289,6,44,12,Female,Yes,No,Caucasian,654
|
||||||
|
28,32.793,4534,333,2,44,16,Male,No,No,African American,467
|
||||||
|
29,186.634,13414,949,2,41,14,Female,No,Yes,African American,1809
|
||||||
|
30,26.813,5611,411,4,55,16,Female,No,No,Caucasian,915
|
||||||
|
31,34.142,5666,413,4,47,5,Female,No,Yes,Caucasian,863
|
||||||
|
32,28.941,2733,210,5,43,16,Male,No,Yes,Asian,0
|
||||||
|
33,134.181,7838,563,2,48,13,Female,No,No,Caucasian,526
|
||||||
|
34,31.367,1829,162,4,30,10,Male,No,Yes,Caucasian,0
|
||||||
|
35,20.15,2646,199,2,25,14,Female,No,Yes,Asian,0
|
||||||
|
36,23.35,2558,220,3,49,12,Female,Yes,No,Caucasian,419
|
||||||
|
37,62.413,6457,455,2,71,11,Female,No,Yes,Caucasian,762
|
||||||
|
38,30.007,6481,462,2,69,9,Female,No,Yes,Caucasian,1093
|
||||||
|
39,11.795,3899,300,4,25,10,Female,No,No,Caucasian,531
|
||||||
|
40,13.647,3461,264,4,47,14,Male,No,Yes,Caucasian,344
|
||||||
|
41,34.95,3327,253,3,54,14,Female,No,No,African American,50
|
||||||
|
42,113.659,7659,538,2,66,15,Male,Yes,Yes,African American,1155
|
||||||
|
43,44.158,4763,351,2,66,13,Female,No,Yes,Asian,385
|
||||||
|
44,36.929,6257,445,1,24,14,Female,No,Yes,Asian,976
|
||||||
|
45,31.861,6375,469,3,25,16,Female,No,Yes,Caucasian,1120
|
||||||
|
46,77.38,7569,564,3,50,12,Female,No,Yes,Caucasian,997
|
||||||
|
47,19.531,5043,376,2,64,16,Female,Yes,Yes,Asian,1241
|
||||||
|
48,44.646,4431,320,2,49,15,Male,Yes,Yes,Caucasian,797
|
||||||
|
49,44.522,2252,205,6,72,15,Male,No,Yes,Asian,0
|
||||||
|
50,43.479,4569,354,4,49,13,Male,Yes,Yes,African American,902
|
||||||
|
51,36.362,5183,376,3,49,15,Male,No,Yes,African American,654
|
||||||
|
52,39.705,3969,301,2,27,20,Male,No,Yes,African American,211
|
||||||
|
53,44.205,5441,394,1,32,12,Male,No,Yes,Caucasian,607
|
||||||
|
54,16.304,5466,413,4,66,10,Male,No,Yes,Asian,957
|
||||||
|
55,15.333,1499,138,2,47,9,Female,No,Yes,Asian,0
|
||||||
|
56,32.916,1786,154,2,60,8,Female,No,Yes,Asian,0
|
||||||
|
57,57.1,4742,372,7,79,18,Female,No,Yes,Asian,379
|
||||||
|
58,76.273,4779,367,4,65,14,Female,No,Yes,Caucasian,133
|
||||||
|
59,10.354,3480,281,2,70,17,Male,No,Yes,Caucasian,333
|
||||||
|
60,51.872,5294,390,4,81,17,Female,No,No,Caucasian,531
|
||||||
|
61,35.51,5198,364,2,35,20,Female,No,No,Asian,631
|
||||||
|
62,21.238,3089,254,3,59,10,Female,No,No,Caucasian,108
|
||||||
|
63,30.682,1671,160,2,77,7,Female,No,No,Caucasian,0
|
||||||
|
64,14.132,2998,251,4,75,17,Male,No,No,Caucasian,133
|
||||||
|
65,32.164,2937,223,2,79,15,Female,No,Yes,African American,0
|
||||||
|
66,12,4160,320,4,28,14,Female,No,Yes,Caucasian,602
|
||||||
|
67,113.829,9704,694,4,38,13,Female,No,Yes,Asian,1388
|
||||||
|
68,11.187,5099,380,4,69,16,Female,No,No,African American,889
|
||||||
|
69,27.847,5619,418,2,78,15,Female,No,Yes,Caucasian,822
|
||||||
|
70,49.502,6819,505,4,55,14,Male,No,Yes,Caucasian,1084
|
||||||
|
71,24.889,3954,318,4,75,12,Male,No,Yes,Caucasian,357
|
||||||
|
72,58.781,7402,538,2,81,12,Female,No,Yes,Asian,1103
|
||||||
|
73,22.939,4923,355,1,47,18,Female,No,Yes,Asian,663
|
||||||
|
74,23.989,4523,338,4,31,15,Male,No,No,Caucasian,601
|
||||||
|
75,16.103,5390,418,4,45,10,Female,No,Yes,Caucasian,945
|
||||||
|
76,33.017,3180,224,2,28,16,Male,No,Yes,African American,29
|
||||||
|
77,30.622,3293,251,1,68,16,Male,Yes,No,Caucasian,532
|
||||||
|
78,20.936,3254,253,1,30,15,Female,No,No,Asian,145
|
||||||
|
79,110.968,6662,468,3,45,11,Female,No,Yes,Caucasian,391
|
||||||
|
80,15.354,2101,171,2,65,14,Male,No,No,Asian,0
|
||||||
|
81,27.369,3449,288,3,40,9,Female,No,Yes,Caucasian,162
|
||||||
|
82,53.48,4263,317,1,83,15,Male,No,No,Caucasian,99
|
||||||
|
83,23.672,4433,344,3,63,11,Male,No,No,Caucasian,503
|
||||||
|
84,19.225,1433,122,3,38,14,Female,No,No,Caucasian,0
|
||||||
|
85,43.54,2906,232,4,69,11,Male,No,No,Caucasian,0
|
||||||
|
86,152.298,12066,828,4,41,12,Female,No,Yes,Asian,1779
|
||||||
|
87,55.367,6340,448,1,33,15,Male,No,Yes,Caucasian,815
|
||||||
|
88,11.741,2271,182,4,59,12,Female,No,No,Asian,0
|
||||||
|
89,15.56,4307,352,4,57,8,Male,No,Yes,African American,579
|
||||||
|
90,59.53,7518,543,3,52,9,Female,No,No,African American,1176
|
||||||
|
91,20.191,5767,431,4,42,16,Male,No,Yes,African American,1023
|
||||||
|
92,48.498,6040,456,3,47,16,Male,No,Yes,Caucasian,812
|
||||||
|
93,30.733,2832,249,4,51,13,Male,No,No,Caucasian,0
|
||||||
|
94,16.479,5435,388,2,26,16,Male,No,No,African American,937
|
||||||
|
95,38.009,3075,245,3,45,15,Female,No,No,African American,0
|
||||||
|
96,14.084,855,120,5,46,17,Female,No,Yes,African American,0
|
||||||
|
97,14.312,5382,367,1,59,17,Male,Yes,No,Asian,1380
|
||||||
|
98,26.067,3388,266,4,74,17,Female,No,Yes,African American,155
|
||||||
|
99,36.295,2963,241,2,68,14,Female,Yes,No,African American,375
|
||||||
|
100,83.851,8494,607,5,47,18,Male,No,No,Caucasian,1311
|
||||||
|
101,21.153,3736,256,1,41,11,Male,No,No,Caucasian,298
|
||||||
|
102,17.976,2433,190,3,70,16,Female,Yes,No,Caucasian,431
|
||||||
|
103,68.713,7582,531,2,56,16,Male,Yes,No,Caucasian,1587
|
||||||
|
104,146.183,9540,682,6,66,15,Male,No,No,Caucasian,1050
|
||||||
|
105,15.846,4768,365,4,53,12,Female,No,No,Caucasian,745
|
||||||
|
106,12.031,3182,259,2,58,18,Female,No,Yes,Caucasian,210
|
||||||
|
107,16.819,1337,115,2,74,15,Male,No,Yes,Asian,0
|
||||||
|
108,39.11,3189,263,3,72,12,Male,No,No,Asian,0
|
||||||
|
109,107.986,6033,449,4,64,14,Male,No,Yes,Caucasian,227
|
||||||
|
110,13.561,3261,279,5,37,19,Male,No,Yes,Asian,297
|
||||||
|
111,34.537,3271,250,3,57,17,Female,No,Yes,Asian,47
|
||||||
|
112,28.575,2959,231,2,60,11,Female,No,No,African American,0
|
||||||
|
113,46.007,6637,491,4,42,14,Male,No,Yes,Caucasian,1046
|
||||||
|
114,69.251,6386,474,4,30,12,Female,No,Yes,Asian,768
|
||||||
|
115,16.482,3326,268,4,41,15,Male,No,No,Caucasian,271
|
||||||
|
116,40.442,4828,369,5,81,8,Female,No,No,African American,510
|
||||||
|
117,35.177,2117,186,3,62,16,Female,No,No,Caucasian,0
|
||||||
|
118,91.362,9113,626,1,47,17,Male,No,Yes,Asian,1341
|
||||||
|
119,27.039,2161,173,3,40,17,Female,No,No,Caucasian,0
|
||||||
|
120,23.012,1410,137,3,81,16,Male,No,No,Caucasian,0
|
||||||
|
121,27.241,1402,128,2,67,15,Female,No,Yes,Asian,0
|
||||||
|
122,148.08,8157,599,2,83,13,Male,No,Yes,Caucasian,454
|
||||||
|
123,62.602,7056,481,1,84,11,Female,No,No,Caucasian,904
|
||||||
|
124,11.808,1300,117,3,77,14,Female,No,No,African American,0
|
||||||
|
125,29.564,2529,192,1,30,12,Female,No,Yes,Caucasian,0
|
||||||
|
126,27.578,2531,195,1,34,15,Female,No,Yes,Caucasian,0
|
||||||
|
127,26.427,5533,433,5,50,15,Female,Yes,Yes,Asian,1404
|
||||||
|
128,57.202,3411,259,3,72,11,Female,No,No,Caucasian,0
|
||||||
|
129,123.299,8376,610,2,89,17,Male,Yes,No,African American,1259
|
||||||
|
130,18.145,3461,279,3,56,15,Male,No,Yes,African American,255
|
||||||
|
131,23.793,3821,281,4,56,12,Female,Yes,Yes,African American,868
|
||||||
|
132,10.726,1568,162,5,46,19,Male,No,Yes,Asian,0
|
||||||
|
133,23.283,5443,407,4,49,13,Male,No,Yes,African American,912
|
||||||
|
134,21.455,5829,427,4,80,12,Female,No,Yes,African American,1018
|
||||||
|
135,34.664,5835,452,3,77,15,Female,No,Yes,African American,835
|
||||||
|
136,44.473,3500,257,3,81,16,Female,No,No,African American,8
|
||||||
|
137,54.663,4116,314,2,70,8,Female,No,No,African American,75
|
||||||
|
138,36.355,3613,278,4,35,9,Male,No,Yes,Asian,187
|
||||||
|
139,21.374,2073,175,2,74,11,Female,No,Yes,Caucasian,0
|
||||||
|
140,107.841,10384,728,3,87,7,Male,No,No,African American,1597
|
||||||
|
141,39.831,6045,459,3,32,12,Female,Yes,Yes,African American,1425
|
||||||
|
142,91.876,6754,483,2,33,10,Male,No,Yes,Caucasian,605
|
||||||
|
143,103.893,7416,549,3,84,17,Male,No,No,Asian,669
|
||||||
|
144,19.636,4896,387,3,64,10,Female,No,No,African American,710
|
||||||
|
145,17.392,2748,228,3,32,14,Male,No,Yes,Caucasian,68
|
||||||
|
146,19.529,4673,341,2,51,14,Male,No,No,Asian,642
|
||||||
|
147,17.055,5110,371,3,55,15,Female,No,Yes,Caucasian,805
|
||||||
|
148,23.857,1501,150,3,56,16,Male,No,Yes,Caucasian,0
|
||||||
|
149,15.184,2420,192,2,69,11,Female,No,Yes,Caucasian,0
|
||||||
|
150,13.444,886,121,5,44,10,Male,No,Yes,Asian,0
|
||||||
|
151,63.931,5728,435,3,28,14,Female,No,Yes,African American,581
|
||||||
|
152,35.864,4831,353,3,66,13,Female,No,Yes,Caucasian,534
|
||||||
|
153,41.419,2120,184,4,24,11,Female,Yes,No,Caucasian,156
|
||||||
|
154,92.112,4612,344,3,32,17,Male,No,No,Caucasian,0
|
||||||
|
155,55.056,3155,235,2,31,16,Male,No,Yes,African American,0
|
||||||
|
156,19.537,1362,143,4,34,9,Female,No,Yes,Asian,0
|
||||||
|
157,31.811,4284,338,5,75,13,Female,No,Yes,Caucasian,429
|
||||||
|
158,56.256,5521,406,2,72,16,Female,Yes,Yes,Caucasian,1020
|
||||||
|
159,42.357,5550,406,2,83,12,Female,No,Yes,Asian,653
|
||||||
|
160,53.319,3000,235,3,53,13,Male,No,No,Asian,0
|
||||||
|
161,12.238,4865,381,5,67,11,Female,No,No,Caucasian,836
|
||||||
|
162,31.353,1705,160,3,81,14,Male,No,Yes,Caucasian,0
|
||||||
|
163,63.809,7530,515,1,56,12,Male,No,Yes,Caucasian,1086
|
||||||
|
164,13.676,2330,203,5,80,16,Female,No,No,African American,0
|
||||||
|
165,76.782,5977,429,4,44,12,Male,No,Yes,Asian,548
|
||||||
|
166,25.383,4527,367,4,46,11,Male,No,Yes,Caucasian,570
|
||||||
|
167,35.691,2880,214,2,35,15,Male,No,No,African American,0
|
||||||
|
168,29.403,2327,178,1,37,14,Female,No,Yes,Caucasian,0
|
||||||
|
169,27.47,2820,219,1,32,11,Female,No,Yes,Asian,0
|
||||||
|
170,27.33,6179,459,4,36,12,Female,No,Yes,Caucasian,1099
|
||||||
|
171,34.772,2021,167,3,57,9,Male,No,No,Asian,0
|
||||||
|
172,36.934,4270,299,1,63,9,Female,No,Yes,Caucasian,283
|
||||||
|
173,76.348,4697,344,4,60,18,Male,No,No,Asian,108
|
||||||
|
174,14.887,4745,339,3,58,12,Male,No,Yes,African American,724
|
||||||
|
175,121.834,10673,750,3,54,16,Male,No,No,African American,1573
|
||||||
|
176,30.132,2168,206,3,52,17,Male,No,No,Caucasian,0
|
||||||
|
177,24.05,2607,221,4,32,18,Male,No,Yes,Caucasian,0
|
||||||
|
178,22.379,3965,292,2,34,14,Female,No,Yes,Asian,384
|
||||||
|
179,28.316,4391,316,2,29,10,Female,No,No,Caucasian,453
|
||||||
|
180,58.026,7499,560,5,67,11,Female,No,No,Caucasian,1237
|
||||||
|
181,10.635,3584,294,5,69,16,Male,No,Yes,Asian,423
|
||||||
|
182,46.102,5180,382,3,81,12,Male,No,Yes,African American,516
|
||||||
|
183,58.929,6420,459,2,66,9,Female,No,Yes,African American,789
|
||||||
|
184,80.861,4090,335,3,29,15,Female,No,Yes,Asian,0
|
||||||
|
185,158.889,11589,805,1,62,17,Female,No,Yes,Caucasian,1448
|
||||||
|
186,30.42,4442,316,1,30,14,Female,No,No,African American,450
|
||||||
|
187,36.472,3806,309,2,52,13,Male,No,No,African American,188
|
||||||
|
188,23.365,2179,167,2,75,15,Male,No,No,Asian,0
|
||||||
|
189,83.869,7667,554,2,83,11,Male,No,No,African American,930
|
||||||
|
190,58.351,4411,326,2,85,16,Female,No,Yes,Caucasian,126
|
||||||
|
191,55.187,5352,385,4,50,17,Female,No,Yes,Caucasian,538
|
||||||
|
192,124.29,9560,701,3,52,17,Female,Yes,No,Asian,1687
|
||||||
|
193,28.508,3933,287,4,56,14,Male,No,Yes,Asian,336
|
||||||
|
194,130.209,10088,730,7,39,19,Female,No,Yes,Caucasian,1426
|
||||||
|
195,30.406,2120,181,2,79,14,Male,No,Yes,African American,0
|
||||||
|
196,23.883,5384,398,2,73,16,Female,No,Yes,African American,802
|
||||||
|
197,93.039,7398,517,1,67,12,Male,No,Yes,African American,749
|
||||||
|
198,50.699,3977,304,2,84,17,Female,No,No,African American,69
|
||||||
|
199,27.349,2000,169,4,51,16,Female,No,Yes,African American,0
|
||||||
|
200,10.403,4159,310,3,43,7,Male,No,Yes,Asian,571
|
||||||
|
201,23.949,5343,383,2,40,18,Male,No,Yes,African American,829
|
||||||
|
202,73.914,7333,529,6,67,15,Female,No,Yes,Caucasian,1048
|
||||||
|
203,21.038,1448,145,2,58,13,Female,No,Yes,Caucasian,0
|
||||||
|
204,68.206,6784,499,5,40,16,Female,Yes,No,African American,1411
|
||||||
|
205,57.337,5310,392,2,45,7,Female,No,No,Caucasian,456
|
||||||
|
206,10.793,3878,321,8,29,13,Male,No,No,Caucasian,638
|
||||||
|
207,23.45,2450,180,2,78,13,Male,No,No,Caucasian,0
|
||||||
|
208,10.842,4391,358,5,37,10,Female,Yes,Yes,Caucasian,1216
|
||||||
|
209,51.345,4327,320,3,46,15,Male,No,No,African American,230
|
||||||
|
210,151.947,9156,642,2,91,11,Female,No,Yes,African American,732
|
||||||
|
211,24.543,3206,243,2,62,12,Female,No,Yes,Caucasian,95
|
||||||
|
212,29.567,5309,397,3,25,15,Male,No,No,Caucasian,799
|
||||||
|
213,39.145,4351,323,2,66,13,Male,No,Yes,Caucasian,308
|
||||||
|
214,39.422,5245,383,2,44,19,Male,No,No,African American,637
|
||||||
|
215,34.909,5289,410,2,62,16,Female,No,Yes,Caucasian,681
|
||||||
|
216,41.025,4229,337,3,79,19,Female,No,Yes,Caucasian,246
|
||||||
|
217,15.476,2762,215,3,60,18,Male,No,No,Asian,52
|
||||||
|
218,12.456,5395,392,3,65,14,Male,No,Yes,Caucasian,955
|
||||||
|
219,10.627,1647,149,2,71,10,Female,Yes,Yes,Asian,195
|
||||||
|
220,38.954,5222,370,4,76,13,Female,No,No,Caucasian,653
|
||||||
|
221,44.847,5765,437,3,53,13,Female,Yes,No,Asian,1246
|
||||||
|
222,98.515,8760,633,5,78,11,Female,No,No,African American,1230
|
||||||
|
223,33.437,6207,451,4,44,9,Male,Yes,No,Caucasian,1549
|
||||||
|
224,27.512,4613,344,5,72,17,Male,No,Yes,Asian,573
|
||||||
|
225,121.709,7818,584,4,50,6,Male,No,Yes,Caucasian,701
|
||||||
|
226,15.079,5673,411,4,28,15,Female,No,Yes,Asian,1075
|
||||||
|
227,59.879,6906,527,6,78,15,Female,No,No,Caucasian,1032
|
||||||
|
228,66.989,5614,430,3,47,14,Female,No,Yes,Caucasian,482
|
||||||
|
229,69.165,4668,341,2,34,11,Female,No,No,African American,156
|
||||||
|
230,69.943,7555,547,3,76,9,Male,No,Yes,Asian,1058
|
||||||
|
231,33.214,5137,387,3,59,9,Male,No,No,African American,661
|
||||||
|
232,25.124,4776,378,4,29,12,Male,No,Yes,Caucasian,657
|
||||||
|
233,15.741,4788,360,1,39,14,Male,No,Yes,Asian,689
|
||||||
|
234,11.603,2278,187,3,71,11,Male,No,Yes,Caucasian,0
|
||||||
|
235,69.656,8244,579,3,41,14,Male,No,Yes,African American,1329
|
||||||
|
236,10.503,2923,232,3,25,18,Female,No,Yes,African American,191
|
||||||
|
237,42.529,4986,369,2,37,11,Male,No,Yes,Asian,489
|
||||||
|
238,60.579,5149,388,5,38,15,Male,No,Yes,Asian,443
|
||||||
|
239,26.532,2910,236,6,58,19,Female,No,Yes,Caucasian,52
|
||||||
|
240,27.952,3557,263,1,35,13,Female,No,Yes,Asian,163
|
||||||
|
241,29.705,3351,262,5,71,14,Female,No,Yes,Asian,148
|
||||||
|
242,15.602,906,103,2,36,11,Male,No,Yes,African American,0
|
||||||
|
243,20.918,1233,128,3,47,18,Female,Yes,Yes,Asian,16
|
||||||
|
244,58.165,6617,460,1,56,12,Female,No,Yes,Caucasian,856
|
||||||
|
245,22.561,1787,147,4,66,15,Female,No,No,Caucasian,0
|
||||||
|
246,34.509,2001,189,5,80,18,Female,No,Yes,African American,0
|
||||||
|
247,19.588,3211,265,4,59,14,Female,No,No,Asian,199
|
||||||
|
248,36.364,2220,188,3,50,19,Male,No,No,Caucasian,0
|
||||||
|
249,15.717,905,93,1,38,16,Male,Yes,Yes,Caucasian,0
|
||||||
|
250,22.574,1551,134,3,43,13,Female,Yes,Yes,Caucasian,98
|
||||||
|
251,10.363,2430,191,2,47,18,Female,No,Yes,Asian,0
|
||||||
|
252,28.474,3202,267,5,66,12,Male,No,Yes,Caucasian,132
|
||||||
|
253,72.945,8603,621,3,64,8,Female,No,No,Caucasian,1355
|
||||||
|
254,85.425,5182,402,6,60,12,Male,No,Yes,African American,218
|
||||||
|
255,36.508,6386,469,4,79,6,Female,No,Yes,Caucasian,1048
|
||||||
|
256,58.063,4221,304,3,50,8,Male,No,No,African American,118
|
||||||
|
257,25.936,1774,135,2,71,14,Female,No,No,Asian,0
|
||||||
|
258,15.629,2493,186,1,60,14,Male,No,Yes,Asian,0
|
||||||
|
259,41.4,2561,215,2,36,14,Male,No,Yes,Caucasian,0
|
||||||
|
260,33.657,6196,450,6,55,9,Female,No,No,Caucasian,1092
|
||||||
|
261,67.937,5184,383,4,63,12,Male,No,Yes,Asian,345
|
||||||
|
262,180.379,9310,665,3,67,8,Female,Yes,Yes,Asian,1050
|
||||||
|
263,10.588,4049,296,1,66,13,Female,No,Yes,Caucasian,465
|
||||||
|
264,29.725,3536,270,2,52,15,Female,No,No,African American,133
|
||||||
|
265,27.999,5107,380,1,55,10,Male,No,Yes,Caucasian,651
|
||||||
|
266,40.885,5013,379,3,46,13,Female,No,Yes,African American,549
|
||||||
|
267,88.83,4952,360,4,86,16,Female,No,Yes,Caucasian,15
|
||||||
|
268,29.638,5833,433,3,29,15,Female,No,Yes,Asian,942
|
||||||
|
269,25.988,1349,142,4,82,12,Male,No,No,Caucasian,0
|
||||||
|
270,39.055,5565,410,4,48,18,Female,No,Yes,Caucasian,772
|
||||||
|
271,15.866,3085,217,1,39,13,Male,No,No,Caucasian,136
|
||||||
|
272,44.978,4866,347,1,30,10,Female,No,No,Caucasian,436
|
||||||
|
273,30.413,3690,299,2,25,15,Female,Yes,No,Asian,728
|
||||||
|
274,16.751,4706,353,6,48,14,Male,Yes,No,Asian,1255
|
||||||
|
275,30.55,5869,439,5,81,9,Female,No,No,African American,967
|
||||||
|
276,163.329,8732,636,3,50,14,Male,No,Yes,Caucasian,529
|
||||||
|
277,23.106,3476,257,2,50,15,Female,No,No,Caucasian,209
|
||||||
|
278,41.532,5000,353,2,50,12,Male,No,Yes,Caucasian,531
|
||||||
|
279,128.04,6982,518,2,78,11,Female,No,Yes,Caucasian,250
|
||||||
|
280,54.319,3063,248,3,59,8,Female,Yes,No,Caucasian,269
|
||||||
|
281,53.401,5319,377,3,35,12,Female,No,No,African American,541
|
||||||
|
282,36.142,1852,183,3,33,13,Female,No,No,African American,0
|
||||||
|
283,63.534,8100,581,2,50,17,Female,No,Yes,Caucasian,1298
|
||||||
|
284,49.927,6396,485,3,75,17,Female,No,Yes,Caucasian,890
|
||||||
|
285,14.711,2047,167,2,67,6,Male,No,Yes,Caucasian,0
|
||||||
|
286,18.967,1626,156,2,41,11,Female,No,Yes,Asian,0
|
||||||
|
287,18.036,1552,142,2,48,15,Female,No,No,Caucasian,0
|
||||||
|
288,60.449,3098,272,4,69,8,Male,No,Yes,Caucasian,0
|
||||||
|
289,16.711,5274,387,3,42,16,Female,No,Yes,Asian,863
|
||||||
|
290,10.852,3907,296,2,30,9,Male,No,No,Caucasian,485
|
||||||
|
291,26.37,3235,268,5,78,11,Male,No,Yes,Asian,159
|
||||||
|
292,24.088,3665,287,4,56,13,Female,No,Yes,Caucasian,309
|
||||||
|
293,51.532,5096,380,2,31,15,Male,No,Yes,Caucasian,481
|
||||||
|
294,140.672,11200,817,7,46,9,Male,No,Yes,African American,1677
|
||||||
|
295,42.915,2532,205,4,42,13,Male,No,Yes,Asian,0
|
||||||
|
296,27.272,1389,149,5,67,10,Female,No,Yes,Caucasian,0
|
||||||
|
297,65.896,5140,370,1,49,17,Female,No,Yes,Caucasian,293
|
||||||
|
298,55.054,4381,321,3,74,17,Male,No,Yes,Asian,188
|
||||||
|
299,20.791,2672,204,1,70,18,Female,No,No,African American,0
|
||||||
|
300,24.919,5051,372,3,76,11,Female,No,Yes,African American,711
|
||||||
|
301,21.786,4632,355,1,50,17,Male,No,Yes,Caucasian,580
|
||||||
|
302,31.335,3526,289,3,38,7,Female,No,No,Caucasian,172
|
||||||
|
303,59.855,4964,365,1,46,13,Female,No,Yes,Caucasian,295
|
||||||
|
304,44.061,4970,352,1,79,11,Male,No,Yes,African American,414
|
||||||
|
305,82.706,7506,536,2,64,13,Female,No,Yes,Asian,905
|
||||||
|
306,24.46,1924,165,2,50,14,Female,No,Yes,Asian,0
|
||||||
|
307,45.12,3762,287,3,80,8,Male,No,Yes,Caucasian,70
|
||||||
|
308,75.406,3874,298,3,41,14,Female,No,Yes,Asian,0
|
||||||
|
309,14.956,4640,332,2,33,6,Male,No,No,Asian,681
|
||||||
|
310,75.257,7010,494,3,34,18,Female,No,Yes,Caucasian,885
|
||||||
|
311,33.694,4891,369,1,52,16,Male,Yes,No,African American,1036
|
||||||
|
312,23.375,5429,396,3,57,15,Female,No,Yes,Caucasian,844
|
||||||
|
313,27.825,5227,386,6,63,11,Male,No,Yes,Caucasian,823
|
||||||
|
314,92.386,7685,534,2,75,18,Female,No,Yes,Asian,843
|
||||||
|
315,115.52,9272,656,2,69,14,Male,No,No,African American,1140
|
||||||
|
316,14.479,3907,296,3,43,16,Male,No,Yes,Caucasian,463
|
||||||
|
317,52.179,7306,522,2,57,14,Male,No,No,Asian,1142
|
||||||
|
318,68.462,4712,340,2,71,16,Male,No,Yes,Caucasian,136
|
||||||
|
319,18.951,1485,129,3,82,13,Female,No,No,Caucasian,0
|
||||||
|
320,27.59,2586,229,5,54,16,Male,No,Yes,African American,0
|
||||||
|
321,16.279,1160,126,3,78,13,Male,Yes,Yes,African American,5
|
||||||
|
322,25.078,3096,236,2,27,15,Female,No,Yes,Caucasian,81
|
||||||
|
323,27.229,3484,282,6,51,11,Male,No,No,Caucasian,265
|
||||||
|
324,182.728,13913,982,4,98,17,Male,No,Yes,Caucasian,1999
|
||||||
|
325,31.029,2863,223,2,66,17,Male,Yes,Yes,Asian,415
|
||||||
|
326,17.765,5072,364,1,66,12,Female,No,Yes,Caucasian,732
|
||||||
|
327,125.48,10230,721,3,82,16,Male,No,Yes,Caucasian,1361
|
||||||
|
328,49.166,6662,508,3,68,14,Female,No,No,Asian,984
|
||||||
|
329,41.192,3673,297,3,54,16,Female,No,Yes,Caucasian,121
|
||||||
|
330,94.193,7576,527,2,44,16,Female,No,Yes,Caucasian,846
|
||||||
|
331,20.405,4543,329,2,72,17,Male,Yes,No,Asian,1054
|
||||||
|
332,12.581,3976,291,2,48,16,Male,No,Yes,Caucasian,474
|
||||||
|
333,62.328,5228,377,3,83,15,Male,No,No,Caucasian,380
|
||||||
|
334,21.011,3402,261,2,68,17,Male,No,Yes,African American,182
|
||||||
|
335,24.23,4756,351,2,64,15,Female,No,Yes,Caucasian,594
|
||||||
|
336,24.314,3409,270,2,23,7,Female,No,Yes,Caucasian,194
|
||||||
|
337,32.856,5884,438,4,68,13,Male,No,No,Caucasian,926
|
||||||
|
338,12.414,855,119,3,32,12,Male,No,Yes,African American,0
|
||||||
|
339,41.365,5303,377,1,45,14,Male,No,No,Caucasian,606
|
||||||
|
340,149.316,10278,707,1,80,16,Male,No,No,African American,1107
|
||||||
|
341,27.794,3807,301,4,35,8,Female,No,Yes,African American,320
|
||||||
|
342,13.234,3922,299,2,77,17,Female,No,Yes,Caucasian,426
|
||||||
|
343,14.595,2955,260,5,37,9,Male,No,Yes,African American,204
|
||||||
|
344,10.735,3746,280,2,44,17,Female,No,Yes,Caucasian,410
|
||||||
|
345,48.218,5199,401,7,39,10,Male,No,Yes,Asian,633
|
||||||
|
346,30.012,1511,137,2,33,17,Male,No,Yes,Caucasian,0
|
||||||
|
347,21.551,5380,420,5,51,18,Male,No,Yes,Asian,907
|
||||||
|
348,160.231,10748,754,2,69,17,Male,No,No,Caucasian,1192
|
||||||
|
349,13.433,1134,112,3,70,14,Male,No,Yes,Caucasian,0
|
||||||
|
350,48.577,5145,389,3,71,13,Female,No,Yes,Asian,503
|
||||||
|
351,30.002,1561,155,4,70,13,Female,No,Yes,Caucasian,0
|
||||||
|
352,61.62,5140,374,1,71,9,Male,No,Yes,Caucasian,302
|
||||||
|
353,104.483,7140,507,2,41,14,Male,No,Yes,African American,583
|
||||||
|
354,41.868,4716,342,2,47,18,Male,No,No,Caucasian,425
|
||||||
|
355,12.068,3873,292,1,44,18,Female,No,Yes,Asian,413
|
||||||
|
356,180.682,11966,832,2,58,8,Female,No,Yes,African American,1405
|
||||||
|
357,34.48,6090,442,3,36,14,Male,No,No,Caucasian,962
|
||||||
|
358,39.609,2539,188,1,40,14,Male,No,Yes,Asian,0
|
||||||
|
359,30.111,4336,339,1,81,18,Male,No,Yes,Caucasian,347
|
||||||
|
360,12.335,4471,344,3,79,12,Male,No,Yes,African American,611
|
||||||
|
361,53.566,5891,434,4,82,10,Female,No,No,Caucasian,712
|
||||||
|
362,53.217,4943,362,2,46,16,Female,No,Yes,Asian,382
|
||||||
|
363,26.162,5101,382,3,62,19,Female,No,No,African American,710
|
||||||
|
364,64.173,6127,433,1,80,10,Male,No,Yes,Caucasian,578
|
||||||
|
365,128.669,9824,685,3,67,16,Male,No,Yes,Asian,1243
|
||||||
|
366,113.772,6442,489,4,69,15,Male,Yes,Yes,Caucasian,790
|
||||||
|
367,61.069,7871,564,3,56,14,Male,No,Yes,Caucasian,1264
|
||||||
|
368,23.793,3615,263,2,70,14,Male,No,No,African American,216
|
||||||
|
369,89,5759,440,3,37,6,Female,No,No,Caucasian,345
|
||||||
|
370,71.682,8028,599,3,57,16,Male,No,Yes,Caucasian,1208
|
||||||
|
371,35.61,6135,466,4,40,12,Male,No,No,Caucasian,992
|
||||||
|
372,39.116,2150,173,4,75,15,Male,No,No,Caucasian,0
|
||||||
|
373,19.782,3782,293,2,46,16,Female,Yes,No,Caucasian,840
|
||||||
|
374,55.412,5354,383,2,37,16,Female,Yes,Yes,Caucasian,1003
|
||||||
|
375,29.4,4840,368,3,76,18,Female,No,Yes,Caucasian,588
|
||||||
|
376,20.974,5673,413,5,44,16,Female,No,Yes,Caucasian,1000
|
||||||
|
377,87.625,7167,515,2,46,10,Female,No,No,African American,767
|
||||||
|
378,28.144,1567,142,3,51,10,Male,No,Yes,Caucasian,0
|
||||||
|
379,19.349,4941,366,1,33,19,Male,No,Yes,Caucasian,717
|
||||||
|
380,53.308,2860,214,1,84,10,Male,No,Yes,Caucasian,0
|
||||||
|
381,115.123,7760,538,3,83,14,Female,No,No,African American,661
|
||||||
|
382,101.788,8029,574,2,84,11,Male,No,Yes,Caucasian,849
|
||||||
|
383,24.824,5495,409,1,33,9,Male,Yes,No,Caucasian,1352
|
||||||
|
384,14.292,3274,282,9,64,9,Male,No,Yes,Caucasian,382
|
||||||
|
385,20.088,1870,180,3,76,16,Male,No,No,African American,0
|
||||||
|
386,26.4,5640,398,3,58,15,Female,No,No,Asian,905
|
||||||
|
387,19.253,3683,287,4,57,10,Male,No,No,African American,371
|
||||||
|
388,16.529,1357,126,3,62,9,Male,No,No,Asian,0
|
||||||
|
389,37.878,6827,482,2,80,13,Female,No,No,Caucasian,1129
|
||||||
|
390,83.948,7100,503,2,44,18,Male,No,No,Caucasian,806
|
||||||
|
391,135.118,10578,747,3,81,15,Female,No,Yes,Asian,1393
|
||||||
|
392,73.327,6555,472,2,43,15,Female,No,No,Caucasian,721
|
||||||
|
393,25.974,2308,196,2,24,10,Male,No,No,Asian,0
|
||||||
|
394,17.316,1335,138,2,65,13,Male,No,No,African American,0
|
||||||
|
395,49.794,5758,410,4,40,8,Male,No,No,Caucasian,734
|
||||||
|
396,12.096,4100,307,3,32,13,Male,No,Yes,Caucasian,560
|
||||||
|
397,13.364,3838,296,5,65,17,Male,No,No,African American,480
|
||||||
|
398,57.872,4171,321,5,67,12,Female,No,Yes,Caucasian,138
|
||||||
|
399,37.728,2525,192,1,44,13,Male,No,Yes,Caucasian,0
|
||||||
|
400,18.701,5524,415,5,64,7,Female,No,No,Asian,966
|
||||||
|
304
datasets/Heart.csv
Normal file
304
datasets/Heart.csv
Normal file
@ -0,0 +1,304 @@
|
|||||||
|
"","Age","Sex","ChestPain","RestBP","Chol","Fbs","RestECG","MaxHR","ExAng","Oldpeak","Slope","Ca","Thal","AHD"
|
||||||
|
"1",63,1,"typical",145,233,1,2,150,0,2.3,3,0,"fixed","No"
|
||||||
|
"2",67,1,"asymptomatic",160,286,0,2,108,1,1.5,2,3,"normal","Yes"
|
||||||
|
"3",67,1,"asymptomatic",120,229,0,2,129,1,2.6,2,2,"reversable","Yes"
|
||||||
|
"4",37,1,"nonanginal",130,250,0,0,187,0,3.5,3,0,"normal","No"
|
||||||
|
"5",41,0,"nontypical",130,204,0,2,172,0,1.4,1,0,"normal","No"
|
||||||
|
"6",56,1,"nontypical",120,236,0,0,178,0,0.8,1,0,"normal","No"
|
||||||
|
"7",62,0,"asymptomatic",140,268,0,2,160,0,3.6,3,2,"normal","Yes"
|
||||||
|
"8",57,0,"asymptomatic",120,354,0,0,163,1,0.6,1,0,"normal","No"
|
||||||
|
"9",63,1,"asymptomatic",130,254,0,2,147,0,1.4,2,1,"reversable","Yes"
|
||||||
|
"10",53,1,"asymptomatic",140,203,1,2,155,1,3.1,3,0,"reversable","Yes"
|
||||||
|
"11",57,1,"asymptomatic",140,192,0,0,148,0,0.4,2,0,"fixed","No"
|
||||||
|
"12",56,0,"nontypical",140,294,0,2,153,0,1.3,2,0,"normal","No"
|
||||||
|
"13",56,1,"nonanginal",130,256,1,2,142,1,0.6,2,1,"fixed","Yes"
|
||||||
|
"14",44,1,"nontypical",120,263,0,0,173,0,0,1,0,"reversable","No"
|
||||||
|
"15",52,1,"nonanginal",172,199,1,0,162,0,0.5,1,0,"reversable","No"
|
||||||
|
"16",57,1,"nonanginal",150,168,0,0,174,0,1.6,1,0,"normal","No"
|
||||||
|
"17",48,1,"nontypical",110,229,0,0,168,0,1,3,0,"reversable","Yes"
|
||||||
|
"18",54,1,"asymptomatic",140,239,0,0,160,0,1.2,1,0,"normal","No"
|
||||||
|
"19",48,0,"nonanginal",130,275,0,0,139,0,0.2,1,0,"normal","No"
|
||||||
|
"20",49,1,"nontypical",130,266,0,0,171,0,0.6,1,0,"normal","No"
|
||||||
|
"21",64,1,"typical",110,211,0,2,144,1,1.8,2,0,"normal","No"
|
||||||
|
"22",58,0,"typical",150,283,1,2,162,0,1,1,0,"normal","No"
|
||||||
|
"23",58,1,"nontypical",120,284,0,2,160,0,1.8,2,0,"normal","Yes"
|
||||||
|
"24",58,1,"nonanginal",132,224,0,2,173,0,3.2,1,2,"reversable","Yes"
|
||||||
|
"25",60,1,"asymptomatic",130,206,0,2,132,1,2.4,2,2,"reversable","Yes"
|
||||||
|
"26",50,0,"nonanginal",120,219,0,0,158,0,1.6,2,0,"normal","No"
|
||||||
|
"27",58,0,"nonanginal",120,340,0,0,172,0,0,1,0,"normal","No"
|
||||||
|
"28",66,0,"typical",150,226,0,0,114,0,2.6,3,0,"normal","No"
|
||||||
|
"29",43,1,"asymptomatic",150,247,0,0,171,0,1.5,1,0,"normal","No"
|
||||||
|
"30",40,1,"asymptomatic",110,167,0,2,114,1,2,2,0,"reversable","Yes"
|
||||||
|
"31",69,0,"typical",140,239,0,0,151,0,1.8,1,2,"normal","No"
|
||||||
|
"32",60,1,"asymptomatic",117,230,1,0,160,1,1.4,1,2,"reversable","Yes"
|
||||||
|
"33",64,1,"nonanginal",140,335,0,0,158,0,0,1,0,"normal","Yes"
|
||||||
|
"34",59,1,"asymptomatic",135,234,0,0,161,0,0.5,2,0,"reversable","No"
|
||||||
|
"35",44,1,"nonanginal",130,233,0,0,179,1,0.4,1,0,"normal","No"
|
||||||
|
"36",42,1,"asymptomatic",140,226,0,0,178,0,0,1,0,"normal","No"
|
||||||
|
"37",43,1,"asymptomatic",120,177,0,2,120,1,2.5,2,0,"reversable","Yes"
|
||||||
|
"38",57,1,"asymptomatic",150,276,0,2,112,1,0.6,2,1,"fixed","Yes"
|
||||||
|
"39",55,1,"asymptomatic",132,353,0,0,132,1,1.2,2,1,"reversable","Yes"
|
||||||
|
"40",61,1,"nonanginal",150,243,1,0,137,1,1,2,0,"normal","No"
|
||||||
|
"41",65,0,"asymptomatic",150,225,0,2,114,0,1,2,3,"reversable","Yes"
|
||||||
|
"42",40,1,"typical",140,199,0,0,178,1,1.4,1,0,"reversable","No"
|
||||||
|
"43",71,0,"nontypical",160,302,0,0,162,0,0.4,1,2,"normal","No"
|
||||||
|
"44",59,1,"nonanginal",150,212,1,0,157,0,1.6,1,0,"normal","No"
|
||||||
|
"45",61,0,"asymptomatic",130,330,0,2,169,0,0,1,0,"normal","Yes"
|
||||||
|
"46",58,1,"nonanginal",112,230,0,2,165,0,2.5,2,1,"reversable","Yes"
|
||||||
|
"47",51,1,"nonanginal",110,175,0,0,123,0,0.6,1,0,"normal","No"
|
||||||
|
"48",50,1,"asymptomatic",150,243,0,2,128,0,2.6,2,0,"reversable","Yes"
|
||||||
|
"49",65,0,"nonanginal",140,417,1,2,157,0,0.8,1,1,"normal","No"
|
||||||
|
"50",53,1,"nonanginal",130,197,1,2,152,0,1.2,3,0,"normal","No"
|
||||||
|
"51",41,0,"nontypical",105,198,0,0,168,0,0,1,1,"normal","No"
|
||||||
|
"52",65,1,"asymptomatic",120,177,0,0,140,0,0.4,1,0,"reversable","No"
|
||||||
|
"53",44,1,"asymptomatic",112,290,0,2,153,0,0,1,1,"normal","Yes"
|
||||||
|
"54",44,1,"nontypical",130,219,0,2,188,0,0,1,0,"normal","No"
|
||||||
|
"55",60,1,"asymptomatic",130,253,0,0,144,1,1.4,1,1,"reversable","Yes"
|
||||||
|
"56",54,1,"asymptomatic",124,266,0,2,109,1,2.2,2,1,"reversable","Yes"
|
||||||
|
"57",50,1,"nonanginal",140,233,0,0,163,0,0.6,2,1,"reversable","Yes"
|
||||||
|
"58",41,1,"asymptomatic",110,172,0,2,158,0,0,1,0,"reversable","Yes"
|
||||||
|
"59",54,1,"nonanginal",125,273,0,2,152,0,0.5,3,1,"normal","No"
|
||||||
|
"60",51,1,"typical",125,213,0,2,125,1,1.4,1,1,"normal","No"
|
||||||
|
"61",51,0,"asymptomatic",130,305,0,0,142,1,1.2,2,0,"reversable","Yes"
|
||||||
|
"62",46,0,"nonanginal",142,177,0,2,160,1,1.4,3,0,"normal","No"
|
||||||
|
"63",58,1,"asymptomatic",128,216,0,2,131,1,2.2,2,3,"reversable","Yes"
|
||||||
|
"64",54,0,"nonanginal",135,304,1,0,170,0,0,1,0,"normal","No"
|
||||||
|
"65",54,1,"asymptomatic",120,188,0,0,113,0,1.4,2,1,"reversable","Yes"
|
||||||
|
"66",60,1,"asymptomatic",145,282,0,2,142,1,2.8,2,2,"reversable","Yes"
|
||||||
|
"67",60,1,"nonanginal",140,185,0,2,155,0,3,2,0,"normal","Yes"
|
||||||
|
"68",54,1,"nonanginal",150,232,0,2,165,0,1.6,1,0,"reversable","No"
|
||||||
|
"69",59,1,"asymptomatic",170,326,0,2,140,1,3.4,3,0,"reversable","Yes"
|
||||||
|
"70",46,1,"nonanginal",150,231,0,0,147,0,3.6,2,0,"normal","Yes"
|
||||||
|
"71",65,0,"nonanginal",155,269,0,0,148,0,0.8,1,0,"normal","No"
|
||||||
|
"72",67,1,"asymptomatic",125,254,1,0,163,0,0.2,2,2,"reversable","Yes"
|
||||||
|
"73",62,1,"asymptomatic",120,267,0,0,99,1,1.8,2,2,"reversable","Yes"
|
||||||
|
"74",65,1,"asymptomatic",110,248,0,2,158,0,0.6,1,2,"fixed","Yes"
|
||||||
|
"75",44,1,"asymptomatic",110,197,0,2,177,0,0,1,1,"normal","Yes"
|
||||||
|
"76",65,0,"nonanginal",160,360,0,2,151,0,0.8,1,0,"normal","No"
|
||||||
|
"77",60,1,"asymptomatic",125,258,0,2,141,1,2.8,2,1,"reversable","Yes"
|
||||||
|
"78",51,0,"nonanginal",140,308,0,2,142,0,1.5,1,1,"normal","No"
|
||||||
|
"79",48,1,"nontypical",130,245,0,2,180,0,0.2,2,0,"normal","No"
|
||||||
|
"80",58,1,"asymptomatic",150,270,0,2,111,1,0.8,1,0,"reversable","Yes"
|
||||||
|
"81",45,1,"asymptomatic",104,208,0,2,148,1,3,2,0,"normal","No"
|
||||||
|
"82",53,0,"asymptomatic",130,264,0,2,143,0,0.4,2,0,"normal","No"
|
||||||
|
"83",39,1,"nonanginal",140,321,0,2,182,0,0,1,0,"normal","No"
|
||||||
|
"84",68,1,"nonanginal",180,274,1,2,150,1,1.6,2,0,"reversable","Yes"
|
||||||
|
"85",52,1,"nontypical",120,325,0,0,172,0,0.2,1,0,"normal","No"
|
||||||
|
"86",44,1,"nonanginal",140,235,0,2,180,0,0,1,0,"normal","No"
|
||||||
|
"87",47,1,"nonanginal",138,257,0,2,156,0,0,1,0,"normal","No"
|
||||||
|
"88",53,0,"nonanginal",128,216,0,2,115,0,0,1,0,NA,"No"
|
||||||
|
"89",53,0,"asymptomatic",138,234,0,2,160,0,0,1,0,"normal","No"
|
||||||
|
"90",51,0,"nonanginal",130,256,0,2,149,0,0.5,1,0,"normal","No"
|
||||||
|
"91",66,1,"asymptomatic",120,302,0,2,151,0,0.4,2,0,"normal","No"
|
||||||
|
"92",62,0,"asymptomatic",160,164,0,2,145,0,6.2,3,3,"reversable","Yes"
|
||||||
|
"93",62,1,"nonanginal",130,231,0,0,146,0,1.8,2,3,"reversable","No"
|
||||||
|
"94",44,0,"nonanginal",108,141,0,0,175,0,0.6,2,0,"normal","No"
|
||||||
|
"95",63,0,"nonanginal",135,252,0,2,172,0,0,1,0,"normal","No"
|
||||||
|
"96",52,1,"asymptomatic",128,255,0,0,161,1,0,1,1,"reversable","Yes"
|
||||||
|
"97",59,1,"asymptomatic",110,239,0,2,142,1,1.2,2,1,"reversable","Yes"
|
||||||
|
"98",60,0,"asymptomatic",150,258,0,2,157,0,2.6,2,2,"reversable","Yes"
|
||||||
|
"99",52,1,"nontypical",134,201,0,0,158,0,0.8,1,1,"normal","No"
|
||||||
|
"100",48,1,"asymptomatic",122,222,0,2,186,0,0,1,0,"normal","No"
|
||||||
|
"101",45,1,"asymptomatic",115,260,0,2,185,0,0,1,0,"normal","No"
|
||||||
|
"102",34,1,"typical",118,182,0,2,174,0,0,1,0,"normal","No"
|
||||||
|
"103",57,0,"asymptomatic",128,303,0,2,159,0,0,1,1,"normal","No"
|
||||||
|
"104",71,0,"nonanginal",110,265,1,2,130,0,0,1,1,"normal","No"
|
||||||
|
"105",49,1,"nonanginal",120,188,0,0,139,0,2,2,3,"reversable","Yes"
|
||||||
|
"106",54,1,"nontypical",108,309,0,0,156,0,0,1,0,"reversable","No"
|
||||||
|
"107",59,1,"asymptomatic",140,177,0,0,162,1,0,1,1,"reversable","Yes"
|
||||||
|
"108",57,1,"nonanginal",128,229,0,2,150,0,0.4,2,1,"reversable","Yes"
|
||||||
|
"109",61,1,"asymptomatic",120,260,0,0,140,1,3.6,2,1,"reversable","Yes"
|
||||||
|
"110",39,1,"asymptomatic",118,219,0,0,140,0,1.2,2,0,"reversable","Yes"
|
||||||
|
"111",61,0,"asymptomatic",145,307,0,2,146,1,1,2,0,"reversable","Yes"
|
||||||
|
"112",56,1,"asymptomatic",125,249,1,2,144,1,1.2,2,1,"normal","Yes"
|
||||||
|
"113",52,1,"typical",118,186,0,2,190,0,0,2,0,"fixed","No"
|
||||||
|
"114",43,0,"asymptomatic",132,341,1,2,136,1,3,2,0,"reversable","Yes"
|
||||||
|
"115",62,0,"nonanginal",130,263,0,0,97,0,1.2,2,1,"reversable","Yes"
|
||||||
|
"116",41,1,"nontypical",135,203,0,0,132,0,0,2,0,"fixed","No"
|
||||||
|
"117",58,1,"nonanginal",140,211,1,2,165,0,0,1,0,"normal","No"
|
||||||
|
"118",35,0,"asymptomatic",138,183,0,0,182,0,1.4,1,0,"normal","No"
|
||||||
|
"119",63,1,"asymptomatic",130,330,1,2,132,1,1.8,1,3,"reversable","Yes"
|
||||||
|
"120",65,1,"asymptomatic",135,254,0,2,127,0,2.8,2,1,"reversable","Yes"
|
||||||
|
"121",48,1,"asymptomatic",130,256,1,2,150,1,0,1,2,"reversable","Yes"
|
||||||
|
"122",63,0,"asymptomatic",150,407,0,2,154,0,4,2,3,"reversable","Yes"
|
||||||
|
"123",51,1,"nonanginal",100,222,0,0,143,1,1.2,2,0,"normal","No"
|
||||||
|
"124",55,1,"asymptomatic",140,217,0,0,111,1,5.6,3,0,"reversable","Yes"
|
||||||
|
"125",65,1,"typical",138,282,1,2,174,0,1.4,2,1,"normal","Yes"
|
||||||
|
"126",45,0,"nontypical",130,234,0,2,175,0,0.6,2,0,"normal","No"
|
||||||
|
"127",56,0,"asymptomatic",200,288,1,2,133,1,4,3,2,"reversable","Yes"
|
||||||
|
"128",54,1,"asymptomatic",110,239,0,0,126,1,2.8,2,1,"reversable","Yes"
|
||||||
|
"129",44,1,"nontypical",120,220,0,0,170,0,0,1,0,"normal","No"
|
||||||
|
"130",62,0,"asymptomatic",124,209,0,0,163,0,0,1,0,"normal","No"
|
||||||
|
"131",54,1,"nonanginal",120,258,0,2,147,0,0.4,2,0,"reversable","No"
|
||||||
|
"132",51,1,"nonanginal",94,227,0,0,154,1,0,1,1,"reversable","No"
|
||||||
|
"133",29,1,"nontypical",130,204,0,2,202,0,0,1,0,"normal","No"
|
||||||
|
"134",51,1,"asymptomatic",140,261,0,2,186,1,0,1,0,"normal","No"
|
||||||
|
"135",43,0,"nonanginal",122,213,0,0,165,0,0.2,2,0,"normal","No"
|
||||||
|
"136",55,0,"nontypical",135,250,0,2,161,0,1.4,2,0,"normal","No"
|
||||||
|
"137",70,1,"asymptomatic",145,174,0,0,125,1,2.6,3,0,"reversable","Yes"
|
||||||
|
"138",62,1,"nontypical",120,281,0,2,103,0,1.4,2,1,"reversable","Yes"
|
||||||
|
"139",35,1,"asymptomatic",120,198,0,0,130,1,1.6,2,0,"reversable","Yes"
|
||||||
|
"140",51,1,"nonanginal",125,245,1,2,166,0,2.4,2,0,"normal","No"
|
||||||
|
"141",59,1,"nontypical",140,221,0,0,164,1,0,1,0,"normal","No"
|
||||||
|
"142",59,1,"typical",170,288,0,2,159,0,0.2,2,0,"reversable","Yes"
|
||||||
|
"143",52,1,"nontypical",128,205,1,0,184,0,0,1,0,"normal","No"
|
||||||
|
"144",64,1,"nonanginal",125,309,0,0,131,1,1.8,2,0,"reversable","Yes"
|
||||||
|
"145",58,1,"nonanginal",105,240,0,2,154,1,0.6,2,0,"reversable","No"
|
||||||
|
"146",47,1,"nonanginal",108,243,0,0,152,0,0,1,0,"normal","Yes"
|
||||||
|
"147",57,1,"asymptomatic",165,289,1,2,124,0,1,2,3,"reversable","Yes"
|
||||||
|
"148",41,1,"nonanginal",112,250,0,0,179,0,0,1,0,"normal","No"
|
||||||
|
"149",45,1,"nontypical",128,308,0,2,170,0,0,1,0,"normal","No"
|
||||||
|
"150",60,0,"nonanginal",102,318,0,0,160,0,0,1,1,"normal","No"
|
||||||
|
"151",52,1,"typical",152,298,1,0,178,0,1.2,2,0,"reversable","No"
|
||||||
|
"152",42,0,"asymptomatic",102,265,0,2,122,0,0.6,2,0,"normal","No"
|
||||||
|
"153",67,0,"nonanginal",115,564,0,2,160,0,1.6,2,0,"reversable","No"
|
||||||
|
"154",55,1,"asymptomatic",160,289,0,2,145,1,0.8,2,1,"reversable","Yes"
|
||||||
|
"155",64,1,"asymptomatic",120,246,0,2,96,1,2.2,3,1,"normal","Yes"
|
||||||
|
"156",70,1,"asymptomatic",130,322,0,2,109,0,2.4,2,3,"normal","Yes"
|
||||||
|
"157",51,1,"asymptomatic",140,299,0,0,173,1,1.6,1,0,"reversable","Yes"
|
||||||
|
"158",58,1,"asymptomatic",125,300,0,2,171,0,0,1,2,"reversable","Yes"
|
||||||
|
"159",60,1,"asymptomatic",140,293,0,2,170,0,1.2,2,2,"reversable","Yes"
|
||||||
|
"160",68,1,"nonanginal",118,277,0,0,151,0,1,1,1,"reversable","No"
|
||||||
|
"161",46,1,"nontypical",101,197,1,0,156,0,0,1,0,"reversable","No"
|
||||||
|
"162",77,1,"asymptomatic",125,304,0,2,162,1,0,1,3,"normal","Yes"
|
||||||
|
"163",54,0,"nonanginal",110,214,0,0,158,0,1.6,2,0,"normal","No"
|
||||||
|
"164",58,0,"asymptomatic",100,248,0,2,122,0,1,2,0,"normal","No"
|
||||||
|
"165",48,1,"nonanginal",124,255,1,0,175,0,0,1,2,"normal","No"
|
||||||
|
"166",57,1,"asymptomatic",132,207,0,0,168,1,0,1,0,"reversable","No"
|
||||||
|
"167",52,1,"nonanginal",138,223,0,0,169,0,0,1,NA,"normal","No"
|
||||||
|
"168",54,0,"nontypical",132,288,1,2,159,1,0,1,1,"normal","No"
|
||||||
|
"169",35,1,"asymptomatic",126,282,0,2,156,1,0,1,0,"reversable","Yes"
|
||||||
|
"170",45,0,"nontypical",112,160,0,0,138,0,0,2,0,"normal","No"
|
||||||
|
"171",70,1,"nonanginal",160,269,0,0,112,1,2.9,2,1,"reversable","Yes"
|
||||||
|
"172",53,1,"asymptomatic",142,226,0,2,111,1,0,1,0,"reversable","No"
|
||||||
|
"173",59,0,"asymptomatic",174,249,0,0,143,1,0,2,0,"normal","Yes"
|
||||||
|
"174",62,0,"asymptomatic",140,394,0,2,157,0,1.2,2,0,"normal","No"
|
||||||
|
"175",64,1,"asymptomatic",145,212,0,2,132,0,2,2,2,"fixed","Yes"
|
||||||
|
"176",57,1,"asymptomatic",152,274,0,0,88,1,1.2,2,1,"reversable","Yes"
|
||||||
|
"177",52,1,"asymptomatic",108,233,1,0,147,0,0.1,1,3,"reversable","No"
|
||||||
|
"178",56,1,"asymptomatic",132,184,0,2,105,1,2.1,2,1,"fixed","Yes"
|
||||||
|
"179",43,1,"nonanginal",130,315,0,0,162,0,1.9,1,1,"normal","No"
|
||||||
|
"180",53,1,"nonanginal",130,246,1,2,173,0,0,1,3,"normal","No"
|
||||||
|
"181",48,1,"asymptomatic",124,274,0,2,166,0,0.5,2,0,"reversable","Yes"
|
||||||
|
"182",56,0,"asymptomatic",134,409,0,2,150,1,1.9,2,2,"reversable","Yes"
|
||||||
|
"183",42,1,"typical",148,244,0,2,178,0,0.8,1,2,"normal","No"
|
||||||
|
"184",59,1,"typical",178,270,0,2,145,0,4.2,3,0,"reversable","No"
|
||||||
|
"185",60,0,"asymptomatic",158,305,0,2,161,0,0,1,0,"normal","Yes"
|
||||||
|
"186",63,0,"nontypical",140,195,0,0,179,0,0,1,2,"normal","No"
|
||||||
|
"187",42,1,"nonanginal",120,240,1,0,194,0,0.8,3,0,"reversable","No"
|
||||||
|
"188",66,1,"nontypical",160,246,0,0,120,1,0,2,3,"fixed","Yes"
|
||||||
|
"189",54,1,"nontypical",192,283,0,2,195,0,0,1,1,"reversable","Yes"
|
||||||
|
"190",69,1,"nonanginal",140,254,0,2,146,0,2,2,3,"reversable","Yes"
|
||||||
|
"191",50,1,"nonanginal",129,196,0,0,163,0,0,1,0,"normal","No"
|
||||||
|
"192",51,1,"asymptomatic",140,298,0,0,122,1,4.2,2,3,"reversable","Yes"
|
||||||
|
"193",43,1,"asymptomatic",132,247,1,2,143,1,0.1,2,NA,"reversable","Yes"
|
||||||
|
"194",62,0,"asymptomatic",138,294,1,0,106,0,1.9,2,3,"normal","Yes"
|
||||||
|
"195",68,0,"nonanginal",120,211,0,2,115,0,1.5,2,0,"normal","No"
|
||||||
|
"196",67,1,"asymptomatic",100,299,0,2,125,1,0.9,2,2,"normal","Yes"
|
||||||
|
"197",69,1,"typical",160,234,1,2,131,0,0.1,2,1,"normal","No"
|
||||||
|
"198",45,0,"asymptomatic",138,236,0,2,152,1,0.2,2,0,"normal","No"
|
||||||
|
"199",50,0,"nontypical",120,244,0,0,162,0,1.1,1,0,"normal","No"
|
||||||
|
"200",59,1,"typical",160,273,0,2,125,0,0,1,0,"normal","Yes"
|
||||||
|
"201",50,0,"asymptomatic",110,254,0,2,159,0,0,1,0,"normal","No"
|
||||||
|
"202",64,0,"asymptomatic",180,325,0,0,154,1,0,1,0,"normal","No"
|
||||||
|
"203",57,1,"nonanginal",150,126,1,0,173,0,0.2,1,1,"reversable","No"
|
||||||
|
"204",64,0,"nonanginal",140,313,0,0,133,0,0.2,1,0,"reversable","No"
|
||||||
|
"205",43,1,"asymptomatic",110,211,0,0,161,0,0,1,0,"reversable","No"
|
||||||
|
"206",45,1,"asymptomatic",142,309,0,2,147,1,0,2,3,"reversable","Yes"
|
||||||
|
"207",58,1,"asymptomatic",128,259,0,2,130,1,3,2,2,"reversable","Yes"
|
||||||
|
"208",50,1,"asymptomatic",144,200,0,2,126,1,0.9,2,0,"reversable","Yes"
|
||||||
|
"209",55,1,"nontypical",130,262,0,0,155,0,0,1,0,"normal","No"
|
||||||
|
"210",62,0,"asymptomatic",150,244,0,0,154,1,1.4,2,0,"normal","Yes"
|
||||||
|
"211",37,0,"nonanginal",120,215,0,0,170,0,0,1,0,"normal","No"
|
||||||
|
"212",38,1,"typical",120,231,0,0,182,1,3.8,2,0,"reversable","Yes"
|
||||||
|
"213",41,1,"nonanginal",130,214,0,2,168,0,2,2,0,"normal","No"
|
||||||
|
"214",66,0,"asymptomatic",178,228,1,0,165,1,1,2,2,"reversable","Yes"
|
||||||
|
"215",52,1,"asymptomatic",112,230,0,0,160,0,0,1,1,"normal","Yes"
|
||||||
|
"216",56,1,"typical",120,193,0,2,162,0,1.9,2,0,"reversable","No"
|
||||||
|
"217",46,0,"nontypical",105,204,0,0,172,0,0,1,0,"normal","No"
|
||||||
|
"218",46,0,"asymptomatic",138,243,0,2,152,1,0,2,0,"normal","No"
|
||||||
|
"219",64,0,"asymptomatic",130,303,0,0,122,0,2,2,2,"normal","No"
|
||||||
|
"220",59,1,"asymptomatic",138,271,0,2,182,0,0,1,0,"normal","No"
|
||||||
|
"221",41,0,"nonanginal",112,268,0,2,172,1,0,1,0,"normal","No"
|
||||||
|
"222",54,0,"nonanginal",108,267,0,2,167,0,0,1,0,"normal","No"
|
||||||
|
"223",39,0,"nonanginal",94,199,0,0,179,0,0,1,0,"normal","No"
|
||||||
|
"224",53,1,"asymptomatic",123,282,0,0,95,1,2,2,2,"reversable","Yes"
|
||||||
|
"225",63,0,"asymptomatic",108,269,0,0,169,1,1.8,2,2,"normal","Yes"
|
||||||
|
"226",34,0,"nontypical",118,210,0,0,192,0,0.7,1,0,"normal","No"
|
||||||
|
"227",47,1,"asymptomatic",112,204,0,0,143,0,0.1,1,0,"normal","No"
|
||||||
|
"228",67,0,"nonanginal",152,277,0,0,172,0,0,1,1,"normal","No"
|
||||||
|
"229",54,1,"asymptomatic",110,206,0,2,108,1,0,2,1,"normal","Yes"
|
||||||
|
"230",66,1,"asymptomatic",112,212,0,2,132,1,0.1,1,1,"normal","Yes"
|
||||||
|
"231",52,0,"nonanginal",136,196,0,2,169,0,0.1,2,0,"normal","No"
|
||||||
|
"232",55,0,"asymptomatic",180,327,0,1,117,1,3.4,2,0,"normal","Yes"
|
||||||
|
"233",49,1,"nonanginal",118,149,0,2,126,0,0.8,1,3,"normal","Yes"
|
||||||
|
"234",74,0,"nontypical",120,269,0,2,121,1,0.2,1,1,"normal","No"
|
||||||
|
"235",54,0,"nonanginal",160,201,0,0,163,0,0,1,1,"normal","No"
|
||||||
|
"236",54,1,"asymptomatic",122,286,0,2,116,1,3.2,2,2,"normal","Yes"
|
||||||
|
"237",56,1,"asymptomatic",130,283,1,2,103,1,1.6,3,0,"reversable","Yes"
|
||||||
|
"238",46,1,"asymptomatic",120,249,0,2,144,0,0.8,1,0,"reversable","Yes"
|
||||||
|
"239",49,0,"nontypical",134,271,0,0,162,0,0,2,0,"normal","No"
|
||||||
|
"240",42,1,"nontypical",120,295,0,0,162,0,0,1,0,"normal","No"
|
||||||
|
"241",41,1,"nontypical",110,235,0,0,153,0,0,1,0,"normal","No"
|
||||||
|
"242",41,0,"nontypical",126,306,0,0,163,0,0,1,0,"normal","No"
|
||||||
|
"243",49,0,"asymptomatic",130,269,0,0,163,0,0,1,0,"normal","No"
|
||||||
|
"244",61,1,"typical",134,234,0,0,145,0,2.6,2,2,"normal","Yes"
|
||||||
|
"245",60,0,"nonanginal",120,178,1,0,96,0,0,1,0,"normal","No"
|
||||||
|
"246",67,1,"asymptomatic",120,237,0,0,71,0,1,2,0,"normal","Yes"
|
||||||
|
"247",58,1,"asymptomatic",100,234,0,0,156,0,0.1,1,1,"reversable","Yes"
|
||||||
|
"248",47,1,"asymptomatic",110,275,0,2,118,1,1,2,1,"normal","Yes"
|
||||||
|
"249",52,1,"asymptomatic",125,212,0,0,168,0,1,1,2,"reversable","Yes"
|
||||||
|
"250",62,1,"nontypical",128,208,1,2,140,0,0,1,0,"normal","No"
|
||||||
|
"251",57,1,"asymptomatic",110,201,0,0,126,1,1.5,2,0,"fixed","No"
|
||||||
|
"252",58,1,"asymptomatic",146,218,0,0,105,0,2,2,1,"reversable","Yes"
|
||||||
|
"253",64,1,"asymptomatic",128,263,0,0,105,1,0.2,2,1,"reversable","No"
|
||||||
|
"254",51,0,"nonanginal",120,295,0,2,157,0,0.6,1,0,"normal","No"
|
||||||
|
"255",43,1,"asymptomatic",115,303,0,0,181,0,1.2,2,0,"normal","No"
|
||||||
|
"256",42,0,"nonanginal",120,209,0,0,173,0,0,2,0,"normal","No"
|
||||||
|
"257",67,0,"asymptomatic",106,223,0,0,142,0,0.3,1,2,"normal","No"
|
||||||
|
"258",76,0,"nonanginal",140,197,0,1,116,0,1.1,2,0,"normal","No"
|
||||||
|
"259",70,1,"nontypical",156,245,0,2,143,0,0,1,0,"normal","No"
|
||||||
|
"260",57,1,"nontypical",124,261,0,0,141,0,0.3,1,0,"reversable","Yes"
|
||||||
|
"261",44,0,"nonanginal",118,242,0,0,149,0,0.3,2,1,"normal","No"
|
||||||
|
"262",58,0,"nontypical",136,319,1,2,152,0,0,1,2,"normal","Yes"
|
||||||
|
"263",60,0,"typical",150,240,0,0,171,0,0.9,1,0,"normal","No"
|
||||||
|
"264",44,1,"nonanginal",120,226,0,0,169,0,0,1,0,"normal","No"
|
||||||
|
"265",61,1,"asymptomatic",138,166,0,2,125,1,3.6,2,1,"normal","Yes"
|
||||||
|
"266",42,1,"asymptomatic",136,315,0,0,125,1,1.8,2,0,"fixed","Yes"
|
||||||
|
"267",52,1,"asymptomatic",128,204,1,0,156,1,1,2,0,NA,"Yes"
|
||||||
|
"268",59,1,"nonanginal",126,218,1,0,134,0,2.2,2,1,"fixed","Yes"
|
||||||
|
"269",40,1,"asymptomatic",152,223,0,0,181,0,0,1,0,"reversable","Yes"
|
||||||
|
"270",42,1,"nonanginal",130,180,0,0,150,0,0,1,0,"normal","No"
|
||||||
|
"271",61,1,"asymptomatic",140,207,0,2,138,1,1.9,1,1,"reversable","Yes"
|
||||||
|
"272",66,1,"asymptomatic",160,228,0,2,138,0,2.3,1,0,"fixed","No"
|
||||||
|
"273",46,1,"asymptomatic",140,311,0,0,120,1,1.8,2,2,"reversable","Yes"
|
||||||
|
"274",71,0,"asymptomatic",112,149,0,0,125,0,1.6,2,0,"normal","No"
|
||||||
|
"275",59,1,"typical",134,204,0,0,162,0,0.8,1,2,"normal","Yes"
|
||||||
|
"276",64,1,"typical",170,227,0,2,155,0,0.6,2,0,"reversable","No"
|
||||||
|
"277",66,0,"nonanginal",146,278,0,2,152,0,0,2,1,"normal","No"
|
||||||
|
"278",39,0,"nonanginal",138,220,0,0,152,0,0,2,0,"normal","No"
|
||||||
|
"279",57,1,"nontypical",154,232,0,2,164,0,0,1,1,"normal","Yes"
|
||||||
|
"280",58,0,"asymptomatic",130,197,0,0,131,0,0.6,2,0,"normal","No"
|
||||||
|
"281",57,1,"asymptomatic",110,335,0,0,143,1,3,2,1,"reversable","Yes"
|
||||||
|
"282",47,1,"nonanginal",130,253,0,0,179,0,0,1,0,"normal","No"
|
||||||
|
"283",55,0,"asymptomatic",128,205,0,1,130,1,2,2,1,"reversable","Yes"
|
||||||
|
"284",35,1,"nontypical",122,192,0,0,174,0,0,1,0,"normal","No"
|
||||||
|
"285",61,1,"asymptomatic",148,203,0,0,161,0,0,1,1,"reversable","Yes"
|
||||||
|
"286",58,1,"asymptomatic",114,318,0,1,140,0,4.4,3,3,"fixed","Yes"
|
||||||
|
"287",58,0,"asymptomatic",170,225,1,2,146,1,2.8,2,2,"fixed","Yes"
|
||||||
|
"288",58,1,"nontypical",125,220,0,0,144,0,0.4,2,NA,"reversable","No"
|
||||||
|
"289",56,1,"nontypical",130,221,0,2,163,0,0,1,0,"reversable","No"
|
||||||
|
"290",56,1,"nontypical",120,240,0,0,169,0,0,3,0,"normal","No"
|
||||||
|
"291",67,1,"nonanginal",152,212,0,2,150,0,0.8,2,0,"reversable","Yes"
|
||||||
|
"292",55,0,"nontypical",132,342,0,0,166,0,1.2,1,0,"normal","No"
|
||||||
|
"293",44,1,"asymptomatic",120,169,0,0,144,1,2.8,3,0,"fixed","Yes"
|
||||||
|
"294",63,1,"asymptomatic",140,187,0,2,144,1,4,1,2,"reversable","Yes"
|
||||||
|
"295",63,0,"asymptomatic",124,197,0,0,136,1,0,2,0,"normal","Yes"
|
||||||
|
"296",41,1,"nontypical",120,157,0,0,182,0,0,1,0,"normal","No"
|
||||||
|
"297",59,1,"asymptomatic",164,176,1,2,90,0,1,2,2,"fixed","Yes"
|
||||||
|
"298",57,0,"asymptomatic",140,241,0,0,123,1,0.2,2,0,"reversable","Yes"
|
||||||
|
"299",45,1,"typical",110,264,0,0,132,0,1.2,2,0,"reversable","Yes"
|
||||||
|
"300",68,1,"asymptomatic",144,193,1,0,141,0,3.4,2,2,"reversable","Yes"
|
||||||
|
"301",57,1,"asymptomatic",130,131,0,0,115,1,1.2,2,1,"reversable","Yes"
|
||||||
|
"302",57,0,"nontypical",130,236,0,2,174,0,0,2,1,"normal","Yes"
|
||||||
|
"303",38,1,"nonanginal",138,175,0,0,173,0,0,1,NA,"normal","No"
|
||||||
|
31
datasets/Income1.csv
Normal file
31
datasets/Income1.csv
Normal file
@ -0,0 +1,31 @@
|
|||||||
|
"","Education","Income"
|
||||||
|
"1",10,26.6588387834389
|
||||||
|
"2",10.4013377926421,27.3064353457772
|
||||||
|
"3",10.8428093645485,22.1324101716143
|
||||||
|
"4",11.2441471571906,21.1698405046065
|
||||||
|
"5",11.6454849498328,15.1926335164307
|
||||||
|
"6",12.0869565217391,26.3989510407284
|
||||||
|
"7",12.4882943143813,17.435306578572
|
||||||
|
"8",12.8896321070234,25.5078852305278
|
||||||
|
"9",13.2909698996656,36.884594694235
|
||||||
|
"10",13.7324414715719,39.666108747637
|
||||||
|
"11",14.133779264214,34.3962805641312
|
||||||
|
"12",14.5351170568562,41.4979935356871
|
||||||
|
"13",14.9765886287625,44.9815748660704
|
||||||
|
"14",15.3779264214047,47.039595257834
|
||||||
|
"15",15.7792642140468,48.2525782901863
|
||||||
|
"16",16.2207357859532,57.0342513373801
|
||||||
|
"17",16.6220735785953,51.4909192102538
|
||||||
|
"18",17.0234113712375,61.3366205527288
|
||||||
|
"19",17.4648829431438,57.581988179306
|
||||||
|
"20",17.866220735786,68.5537140185881
|
||||||
|
"21",18.2675585284281,64.310925303692
|
||||||
|
"22",18.7090301003344,68.9590086393083
|
||||||
|
"23",19.1103678929766,74.6146392793647
|
||||||
|
"24",19.5117056856187,71.8671953042483
|
||||||
|
"25",19.9130434782609,76.098135379724
|
||||||
|
"26",20.3545150501672,75.77521802986
|
||||||
|
"27",20.7558528428094,72.4860553152424
|
||||||
|
"28",21.1571906354515,77.3550205741877
|
||||||
|
"29",21.5986622073579,72.1187904524136
|
||||||
|
"30",22,80.2605705009016
|
||||||
|
31
datasets/Income2.csv
Normal file
31
datasets/Income2.csv
Normal file
@ -0,0 +1,31 @@
|
|||||||
|
"","Education","Seniority","Income"
|
||||||
|
"1",21.5862068965517,113.103448275862,99.9171726114381
|
||||||
|
"2",18.2758620689655,119.310344827586,92.579134855529
|
||||||
|
"3",12.0689655172414,100.689655172414,34.6787271520874
|
||||||
|
"4",17.0344827586207,187.586206896552,78.7028062353695
|
||||||
|
"5",19.9310344827586,20,68.0099216471551
|
||||||
|
"6",18.2758620689655,26.2068965517241,71.5044853814318
|
||||||
|
"7",19.9310344827586,150.344827586207,87.9704669939115
|
||||||
|
"8",21.1724137931034,82.0689655172414,79.8110298331255
|
||||||
|
"9",20.3448275862069,88.2758620689655,90.00632710858
|
||||||
|
"10",10,113.103448275862,45.6555294997364
|
||||||
|
"11",13.7241379310345,51.0344827586207,31.9138079371295
|
||||||
|
"12",18.6896551724138,144.137931034483,96.2829968022869
|
||||||
|
"13",11.6551724137931,20,27.9825049000603
|
||||||
|
"14",16.6206896551724,94.4827586206897,66.601792415137
|
||||||
|
"15",10,187.586206896552,41.5319924201478
|
||||||
|
"16",20.3448275862069,94.4827586206897,89.00070081522
|
||||||
|
"17",14.1379310344828,20,28.8163007592387
|
||||||
|
"18",16.6206896551724,44.8275862068966,57.6816942573605
|
||||||
|
"19",16.6206896551724,175.172413793103,70.1050960424457
|
||||||
|
"20",20.3448275862069,187.586206896552,98.8340115435447
|
||||||
|
"21",18.2758620689655,100.689655172414,74.7046991976891
|
||||||
|
"22",14.551724137931,137.931034482759,53.5321056283034
|
||||||
|
"23",17.448275862069,94.4827586206897,72.0789236655191
|
||||||
|
"24",10.4137931034483,32.4137931034483,18.5706650327685
|
||||||
|
"25",21.5862068965517,20,78.8057842852386
|
||||||
|
"26",11.2413793103448,44.8275862068966,21.388561306174
|
||||||
|
"27",19.9310344827586,168.965517241379,90.8140351180409
|
||||||
|
"28",11.6551724137931,57.2413793103448,22.6361626208955
|
||||||
|
"29",12.0689655172414,32.4137931034483,17.613593041445
|
||||||
|
"30",17.0344827586207,106.896551724138,74.6109601985289
|
||||||
|
139
islr-ch3.md
Normal file
139
islr-ch3.md
Normal file
@ -0,0 +1,139 @@
|
|||||||
|
# Linear regression
|
||||||
|
|
||||||
|
Though it may seem somewhat dull compared to some of the more modern statistical learning approaches described in later chapters of this book, linear regression is still a useful and widely used statistical learning method. Moreover, it serves as a good jumping-off point for newer approaches: as we will see in later chapters, many fancy statistical learning approaches can be seen as generalizations or extensions of linear regression. Consequently, the importance of having a good understanding of linear regression before studying more complex learning methods cannot be overstated.
|
||||||
|
|
||||||
|
## 3.1 Simple linear regression
|
||||||
|
is a very straightforward simple linear approach for predicting a quantitative response Y on the basis of a single predictor variable X. It assumes that there is approximately a linear relationship between X and Y
|
||||||
|
$$
|
||||||
|
Y \approx \beta_0 + \beta_1 X
|
||||||
|
$$
|
||||||
|
Which we can read as Y is approximately modeled as, or we are regressing Y onto x.
|
||||||
|
In this case with just 2 parameters, beta0 is the intercept and beta1 is the slope.
|
||||||
|
|
||||||
|
### 3.1.1 Estimating coefficients
|
||||||
|
We can create observation pairs $(x_i, y_i)$, and then obtain coefficient estimates such that f(xi) approximates yi. There are different ways to measure *closeness*; The prevailing method is the least squares method.
|
||||||
|
|
||||||
|
Let ŷi = β̂0 + β̂1 xi be the prediction for Y based on the ith value of X.
|
||||||
|
Then ei = yi − ŷ i represents the ith residual—this is the difference between the ith observed response value and the ith response value that is predicted by our linear model. We define the residual sum of squares (RSS) as follows:
|
||||||
|
$$
|
||||||
|
RSS = e_1^2 + e_2^2 + \ldots + e_n^2
|
||||||
|
$$
|
||||||
|
Using some calculus, we can show that
|
||||||
|
|
||||||
|
$$
|
||||||
|
\hat{\beta}_1 = {\sum (x_i = \bar{x})(y_i - \bar{y})\over \sum (x_i - \bar{x})^2} \\
|
||||||
|
\hat{\beta}_0 = \bar{y} - \beta_1 \bar{x}
|
||||||
|
$$
|
||||||
|
|
||||||
|
### 3.1.2 Assessing the acuracy
|
||||||
|
Recall that expected Y was f + irreducable error. This means that for our simple linear regression,
|
||||||
|
$\hat{Y} = \beta_0 + \beta_1 X + \epsilon$
|
||||||
|
|
||||||
|
The error term is a catch-all for what we miss with this simple model: the true relationship is probably not linear, there may be other variables that cause variation in Y , and there may be measurement error. We typically assume that the error term is independent of X.
|
||||||
|
The model defines the population regression line(the real model without error), which is the best linear approximation to the true relationship between X and Y.
|
||||||
|
in real applications, we have access to a set of observations from which we can compute the least squares line; however, the population regression line is unobserved.
|
||||||
|
Typically, the training data is sampled multiple times so we can compute multiple least square lines. This allows us to decrease bias, because each line will be higher or lower, meaning no systematic bias. Kinda like population mean and sample mean.
|
||||||
|
To go futher with our mean analogy, how do we compute how far the sample mean is from the actual mean? We do this with standard error
|
||||||
|
|
||||||
|
$$SE(\hat{\mu})^2 = {\sigma^2 \over n}$$
|
||||||
|
|
||||||
|
similarly,
|
||||||
|
|
||||||
|
$\sigma^2$ is generally not known, but we can use an estimate called **residual standard error** which is calculated as follows:
|
||||||
|
$$RSE = \sqrt{RSS/(n-2)}$$
|
||||||
|
|
||||||
|
Standard errors can be used to compute **confidence intervals**.
|
||||||
|
For linear regression, the 95 % confidence interval for β1
|
||||||
|
approximately takes the form
|
||||||
|
|
||||||
|
the factor of 2 in front of the SE(β̂1 ) term will vary slightly depending on the number of observations n in the linear regression. To be precise, rather than the number 2, it should contain the 97.5 % quantile of a t-distribution with n−2 degrees of freedom.
|
||||||
|
We can also use this for hypothesis testing. To test the null hypothesis, we need to determine whether β̂1 , our estimate for β1 , is sufficiently far from zero that we can be confident that β1 is non-zero. How far depends on SE(β̂1). small SE allows for small numbers, and in contrast, if SE is large, we need a large b1 to tell us that b1 = 0.
|
||||||
|
We're actually computing the t statistic,
|
||||||
|
$$
|
||||||
|
t = {\hat{\beta_1} - 0\over SE(\beta_1)}$$
|
||||||
|
which measures the number of standard deviations that β̂1 is away from 0. We can also compute the p-value.
|
||||||
|
|
||||||
|
### 3.1.3 Assessing the accuracy of our model
|
||||||
|
Once we've rejected the null hypotheses, we would likely want to know to what extend our model fits the data. The quality of a linear regression fit is typically assessed using two related quantities: the residual standard error (RSE) and the $R^2$ Rstatistic.
|
||||||
|
|
||||||
|
### RSE
|
||||||
|
Recall that because of the irreducable error, we won't be able to perfectly predict Y anyway. RSE is an estimate of the std of $\epsilon$. Roughly speaking, it is the average amount that the response
|
||||||
|
will deviate from the true regression line. It is computed using the formula
|
||||||
|
|
||||||
|
In table 3.2, we have an RSE of 3.26. Another way to think about this is that even if the model were correct and the true values of the unknown coefficients β0 and β1 were known exactly, any prediction of sales on the basis of TV
|
||||||
|
advertising would still be off by about 3.260 units on average.
|
||||||
|
It depends on the context wether this is acceptable. In the advertising data set, the mean value of sales over all markets is approximately 1 units, and so the percentage error is
|
||||||
|
3.260/14 = 23 %.
|
||||||
|
The RSE is considered a measure of the lack of fit of the model to the data. As it increases, we know that predicted response will be further of from true response.
|
||||||
|
|
||||||
|
### R^2 statistic
|
||||||
|
The RSE provides an absolute measure of lack of fit of the model to the data. But since it is measured in the units of Y , it is not always clear what constitutes a good RSE. The $R^2$ statistic provides an alternative measure of fit. It takes the form of a proportion—the proportion of variance explained—and so it always takes on a value between 0 and 1, and is independent of the scale of Y .
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
TTS is the total variance in Y. TSS - RSS is the ammount of variability that can be explained with the regression. $R^2$ is the proportion of the variability of Y that can be explained with X. Higher is better. If it's low, regression did not explain much of the variability, and may be due the fact that the real world problem isnt linear at all or that the inherent error $\sigma^2$ is high.
|
||||||
|
The pro is that it's way more interpretable than RSE. How close to 1 is acceptable depends on the context. In physics, a number that's not extremely close to 1 might indicate a serious problem with the experiment, but in biology, sociology, etc, a value of 0.1 might be realistic. Also, correlation(r) is another good measure. In simple linear regression, $R^2 = r^2$
|
||||||
|
Note that this is only for simple lin. regression. For multiple linear regression, we need $R^2$
|
||||||
|
|
||||||
|
## 3.2 Multiple regression
|
||||||
|
How can we extend our simple linear regression model to radio, tv and newspaper advertisments?
|
||||||
|
We could simply make 3 simple lin. regression models, but this would be kinda wrong. Not only are we then ignoring the effects of the other 2 predictors for each model, we also remove the way to predict sales when multiple predictors are tweaked.
|
||||||
|
The better thing to do is to extend our model to include multiple predictors, like so:
|
||||||
|
|
||||||
|
$$
|
||||||
|
Y = \beta_0 + \beta_1 X_1 + \ldots + \beta_p X_p
|
||||||
|
$$
|
||||||
|
|
||||||
|
Any Xj then represents the jth predictor, and $\beta_j$ the average effect of Xj on the response.
|
||||||
|
|
||||||
|
### 3.2.1 Estimating the regression coefficients
|
||||||
|
|
||||||
|
We use the same least squares method for this. that is:
|
||||||
|
$$
|
||||||
|
RSS = \sum (y_i - \hat{y}_i)^2 = \sum (y_i - \beta_0 - \beta_1 X_1 - \ldots - \beta_p X_p)^2
|
||||||
|
$$
|
||||||
|
The formulla used to calculate the coefficients is out of the scope of this book.
|
||||||
|
|
||||||
|
### 3.2.2 Some important Questions
|
||||||
|
#### Is There a Relationship Between the Response and Predictors?
|
||||||
|
|
||||||
|
To answer this, we test the null hypothesis:
|
||||||
|
H0 : every $X_i$ is zero.
|
||||||
|
We do this with the **F-statistic**
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
Hence, when there is no relationship between the response and predictors,
|
||||||
|
one would expect the F-statistic to take on a value close to 1, and a lot greater than 1 otherwise.
|
||||||
|
|
||||||
|
The larger the number of datapoints n, the smaller F has to be to reject the null hypothesis. Every good software package provides a way to calculate the **p-value** associated with the F-statistic using this distribution. Based on this p-value, we can determine whether or not to reject H0 .
|
||||||
|
|
||||||
|
Sometimes we only want to see wether a subset q of the coefficients is zero. We just create a model with only those subset of predictors, and do the same analysis as above, but this time,
|
||||||
|
|
||||||
|
|
||||||
|
**if p > n, we can't fit the linear regression model with least squares, so we don't use the F statistic, or most concepts discussed in this chapter. When p is large, some of the approaches discussed in the next section, such as *forward selection*, can be used. This *high-dimensional* setting will be discussed later.**
|
||||||
|
|
||||||
|
### Do all the predictors help to explain Y , or is only a subset of the predictors useful?
|
||||||
|
|
||||||
|
*Variable selection*, the practice of determining which predictors are associated with the response, in order to fit a single model involving only those predictors is extensively discussed in Ch6, but we'll go a bit in it here.
|
||||||
|
|
||||||
|
|
||||||
|
Unfortunately, we need to fit and test $2^p$ models, which might be very impractical, so we need an automated and efficient approach.
|
||||||
|
There are 3 classical approaches available:
|
||||||
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* Forward selection. We start with a model with no predictors. We then test simple regression models for all p, selecting the one with the lowest RSS. We then test all models with 2 variables containing the previous one, again selecting the one with the lowest RSS. We keep this up untill some stopping rule says we stop (e.g. we only want 5 vars).
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* Backward selection. We start with a model with all predictors, removing the predictor with the largest p-value(The one that's the least statistically significant) and refitting the model. We continue this untill some stopping condition is satisifed, such as when all predictors are below some p-value treshold.
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* Mixed selection. Combination of the 2 mentioned already. We start with forward selection, adding statistically relevant variables. The p-values can change when new predictors are added, so we remove the variable when it's above some treshold. We continue to perform these forward and backward steps until all variables in the model have a sufficiently low p-value, and all variables outside the model would have a large p-value if added to the model.
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**Backward selection cannot be used if p > n, while forward selection can always be used. Forward selection is a greedy approach, and might include variables early that later become redundant. Mixed selection can remedy this.**
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### How well does the model fit the data?
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### Given a set of predictor values, what response value should we predict, and how accurate is our prediction?
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5
quickR/atest.R
Normal file
5
quickR/atest.R
Normal file
@ -0,0 +1,5 @@
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library(ISLR)
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pairs(College[,1:10])
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|
|
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Loading…
Reference in New Issue
Block a user