Problem 4-22
The following data give the selling price, square footage, number of bedrooms and age of houses that have sold in a neighborhood in the past 6 months. Develop 3 regression models to predict the selling price based upon each of the other factors individually. Which of these is best?
Selling price Square footageBedrooms Average years
84,000 1,670 2 30
79,000 1,339 2 25
91,500 1,712 3 30
120,0001,840 3 40
127,5002,300 3 18
132,5002,234 3 30
145,0002,311 3 19
164,0002,377 3 7
155,0002,736 4 10
168,0002,500 3 1
172,5002,500 4 3
174,0002,479 3 3
175,0002,400 3 1
177,5003,124 4 0
184,0002,500 3 2
195,5004,062 4 10
195,0002,854 3 3
Problem 4-23
Use the data in problem 4-22 and develop a regression model to predict selling price based on the square footage and number of bedrooms. Use this to predict the selling price of a 2,000 square foot house with three bedrooms. Compare this model with the models in problem 4-22. Should the number of bedrooms be included in the model? Why or why not?
1. State the linear equation.
2. Explain the overall statistical significance of the model.
3. Explain the statistical significance for each independent variable in the model.
4. Interpret the adjusted R2.
5. Is this a good predictive equation(s)? Which variables should be excluded, if any, and why? Explain.
Problem 4-24
Use the data in problem 4-22 and develop a regression model to predict selling price based on the square footage, number of bedrooms, and age. Use this to predict the selling price of a 10 year old, 2,000 square foot house with three bedrooms.
Problem 4-30
In 2012 the total payroll for the New York Yankees was almost $200 million, while the total payroll for the Oakland Athletics (a team known for using baseball analytics or sabermetrics) was about $55 million, less than 1/3 of the Yankees payroll. In the following table, you will see the payrolls in millions and the total number of victories for the baseball teams in the American League in the 2012 season. Develop a regression model to predict the total number of victories based on the payroll. Use the model to predict the number of victories for a team with a payroll of $79 million. Based on the results of the computer output, discuss the relationship between payroll and victories.
Team Payroll in millions Number of victories
Baltimore Orioles 81.4 93
Boston Red Sox 173.2 69
Chicago White Sox 96.9 85
Cleveland Indians 78.4 68
Detroit Tigers 132.3 88
Kansas City Royals 60.9 72
Los Angeles Angels 154.5 89
Minnesota Twins 94.1 66
NY Yankees 198.0 95
Oakland Athletics 55.4 94
Seattle Mariners 82.0 75
Tampa Bay Rays 64.2 90
Texas Rangers 120.5 93
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