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Calculate correlations; least squares lines, make prediction

See attached Minitab file.

The data set CSDATA contains information about all 224 students who entered a large university in a single year and who planned to major in a computer science. We are interested in predicting GPA (grade point average) after three semesters of college from information available before the student enters college. To do this effectively, we must use several explanatory variables together. This is multiple regression. In this Case Study, You will look at the individual explanatory variables.

A. Correlations. What are the correlations of all the explanatory variables with GPA? Explain why knowing the correlations tell us which variables will best predict GPA in a regression with just one explanatory variable. What are the two best predictor variables? Does your finding seem reasonable for computer science majors?

B. Prediction. Make scatterplots, with the least-squares line added, for GPA versus each of the two best explanatory variables. How well do each of these variables predict GPA? Do these scatterplots contain unusual observations? In what way is each of these observations

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The data set CSDATA contains information about all 224 students who entered a large university in a single year and who planned to major in a computer science. We are interested in predicting GPA (grade point average) after three semesters of college from information available before the student enters college. To do this effectively, we must use several explanatory variables together. This is multiple regression. In this Case Study, You will look at the individual explanatory variables. Calculate Correlations; construct least squares lines, make prediction and plots

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