# The following data give the selling price, square footage, number of

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.

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

Toronto Blue Jays            75.5                                        73

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