STAT630 University of Boston Week 5 Trend Line Equation Questions


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Question 1. 50 points

The data contain midterm and final exam scores for 96 students in a corporate finance course.

Each row contains the two exam scores for a given student, so you might expect them to be positively correlated.

A.Create a scatterplot of the final exam score (Y) versus the midterm score (X).
Based on the visual evidence, would you say that the scores for the two exams are strongly related?
Is the relationship a linear one?

B.Superimpose a trend line on the scatterplot.
Find the trend line equation by using the option to display the equation. Let X represent the midterm.
Find the R2 value by using the option to display the R2value.
What does this equation indicate in terms of predicting a student’s final exam score from his or her midterm score? Be specific.

C.Run a regression to confirm the trend-line equation from Part B. Let X represent the midterm.
What does the standard error of estimate say about the accuracy of the prediction requested in Part B?

Question 2. 50 points

The data is for the 50 top graduate programs in the United States, according to a recent U.S. News & World Report survey. Columns B, C, and D contain ratings: an overall rating, a rating by peer schools, and a rating by recruiters. The other columns contain data that might be related to these ratings.

A.Find a table of correlations between all of the numerical variables.
From these correlations, which variables in columns E–L are most highly correlated with the various ratings?

B.For the Overall rating, run (four separate) simple regressions using it as the dependent variable and GMAT, Accept Rate, Salary, and Enrollment as independent variables.
Interpret these equations.
Could you have guessed the value of R2 before running the regression?
What does the standard error of estimate indicate?

C.Repeat Part B with the Peers rating as the dependent variable.

D.Repeat Part B again with the Recruiters rating as the dependent variable.

E.Discuss any differences among the three sets of regressions.

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