Description
In this problem, you will develop a model to predict whether a given car gets high or
low gas mileage based on the dataset “auto.csv”. The dataset contains 392 observations.
The response variable is mpg cat, which indicates whether the miles per gallon of a car is
high or low. The predictors are:
• cylinders: Number of cylinders between 4 and 8
• displacement: Engine displacement (cu. inches)
• horsepower: Engine horsepower
• weight: Vehicle weight (lbs.)
• acceleration: Time to accelerate from 0 to 60 mph (sec.)
• year: Model year (modulo 100)
• origin: Origin of car (1. American, 2. European, 3. Japanese)
Split the dataset into two parts: training data (70%) and test data (30%).
(a) Produce some graphical or numerical summaries of the data.
(b) Perform a logistic regression using the training data. Do any of the predictors appear
to be statistically significant? If so, which ones? Compute the confusion matrix and
overall fraction of correct predictions using the test data. Briefly explain what the
confusion matrix is telling you.
1 P8106 Homework 3
(c) Train a multivariate adaptive regression spline (MARS) model using the training
data.
(d) Perform LDA using the training data. Plot the linear discriminants in LDA.
(e) Which model will you use to predict the response variable? Plot its ROC curve using
the test data. Report the AUC and the misclassification error rate.
2 P8106 Homework 3
