  Fall Semester 2003

Our ninth time in lab, Wednesday December 3, 2003
Date
Dec  3, 2003

Due today
Reading Assignment: Chapter 9 (Multiple Regression) in the text.

Starting today LAB ASSIGNMENT SIX (the questions listed below).

Due next time (a week from today)
Reading Assignment: Chapter 10 (Analysis of Variance) in the text.

What is the lab assignment?
Answer the questions below also indicating the page(s) in the book where the answer can be found.

When is it due?
Check the What's Due? page for details.

What's the best approach to this assignment?
Read the book, work through all the experiments.

Here are the questions

Chapter 9: Multiple Regression.

1. Consider the `Calc.xls` data from Chapter 8, which were collected to see how performance in freshman calculus is related to various predictors. What was the best individual predictor of the first semester calculus score? Was it a very successful predictor? What does multiple regression aim to calculate?

2. Assume the analysis presented in the book and consider a male student who did not take calculus in high school, scored 30 on his ACT Math exam, scored 23 on his algebra placement test, had a 4.0 grade in second-year high school algebra, and was ranked in the 90th percentile in hos high-school graduation class. What would you predict his calculus score to be? Why. Explain your answer.

3. Is there a difference between male and female students regarding performance in first-year college calculus?

4. How could you reduce the number of predictors (assuming there are non-significant variables).

5. Name and briefly describe the four common plots that can help you assess the success of the regression. How does the use of each one of them translate into conclusions with respect to the example discussed here? (That is, consider questions such as these: Does it appear that the range of values is narrower for large values of predicted calculus score? What does plotting the residuals vs. predicted values tell us? Same questions for the plotting of residuals against the predictor variables. Is there any normality assumption you need to test? Does the assumption seem reasonable in our case here?)

6. Some of the female faculty at a junior college felt underpaid, and they sought statistical help in proving their case. The college collected data for the variables that influence salary for 37 females and 44 males. The data are stored in the file `Discrim.xls`. You are to find out if the female faculty have been treated unfairly or not. What do you do and what are your most exhaustive conclusions? Provide a summary with explanations.

7. Is there a discrepancy between the male faculty and female faculty? What is its average value? Is there a variable that has a greatest influence on this average discrepancy? How do you know? Explain.

Last updated: Nov 10, 2003 by Adrian German for `A113`