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.
USA 
India