Complete: Chapter 4, pp. 142-143 - Complete Problems 4-22 and 4-23.
Chapter 4, page 143 - Complete Problem 4-24. In addition to the questions in this problem, respond to the following:
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.
Chapter 4, pages 144-145 - Complete Problem 4-32.
Use Excel's regression option to perform the regression. Use one Excel spreadsheet file for the calculations and explanations, with one worksheet per problem. Use the problem number for each worksheet name. Cells should contain the formulas (i.e., if a formula was used to calculate the entry in that cell).
4-22The 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 three regression models to predict
the selling price based upon each of the other factors
individually. Which of these is best?
SELLING SQUARE AGE
PRICE($) FOOTAGE BEDROOMS (YEARS)
64,000 1,670 230
59,000 1,339 2 25
61,500 1,712 3 30
79,000 1,840 3 40
87,500 2,300 3 18
92,500 2,234 3 30
95,000 2,311 3 19
113,000 2,377 3 7
(Continued on next page)
I
SELLING SQUARE AGE
PRICE($) FOOTAGE BEDROOMS (YEARS)
115,000 2,736 4 10
138,000 2,500 3 1
142,500 2,500 4 3
144,000 2,479 3 3
145,000 2,400 3 1
147,500 3,124 4 0
144,000 2,500 3 2
155,500 4,062 4 10
165,000 2,854 3 3
4-23Use 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 3 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?
4-24Use 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 3 bedrooms.
In addition to the questions in this problem, respond to the following:
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.
Is this a good predictive equation(s)? Which variables should be excluded (if any) and why? Explain.
4-32In 2009, the New York Yankees won 103 baseball
games during the regular season. The table on the next
page lists the number of victories (W), the earnedrun-
average (ERA), and the batting average (AVG)
of each team in the American League. The ERA is
one measure of the effectiveness of the pitching staff,
and a lower number is better. The batting average
is one measure of effectiveness of the hitters, and a
higher number is better.
(a) Develop a regression model that could be used to
predict the number of victories based on the ERA.
(b) Develop a regression model that could be used to
predict the number of victories based on the batting
average.
TEAM W ERA AVG
New York Yankees 103 4.26 0.283
Los Angeles Angels 97 4.45 0.285
Boston Red Sox 95 4.35 0.270
Minnesota Twins 87 4.50 0.274
Texas Rangers 87 4.38 0.260
Detroit Tigers 864.29 0.260
Seattle Mariners 85 3.87 0.258
Tampa Bay Rays 84 4.33 0.263
Chicago White Sox 79 4.14 0.258
Toronto Blue Jays 75 4.47 0.266
Oakland Athletics 75 4.26 0.262
Cleveland Indians 65 5.06 0.264
Kansas City Royals 65 4.83 0.259
Baltimore Orioles 64 5.15 0.268
(c) Which of the two models is better for predicting
the number of victories?
(d) Develop a multiple regression model that includes
both ERA and batting average. How does
this compare to the previous models?
USA 
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