A real estate builder wishes to determine how house size (House) is influenced by family income (Income), family size (Size), and education of the head of household (School). House size is measured in hundreds of square feet, income is measured in thousands of dollars, and education is in years. The builder randomly selected 50 families and ran the multiple regression. Microsoft Excel output is provided below
Multiple R 0.865
R Square 0.748
Adjusted R Square 0.726
Standard Error 5.195
Df SS MS F Signif F
Regression 3605.7736 1201.9245 0.0000
Residual 1214.2264 26.3962
Total 49 4820.0000
Coeff StdError t Stat p-value
Intercept -1.6335 5.8078 -0.281 0.7798
Income 0.4485 0.1137 3.9545 0.0003
Size 4.2615 0.8062 5.286 0.0001
School -0.6517 0.4319 -1.509 0.1383
2. Referring to Table 12-4, which of the following values for the level of significance is the smallest for which the regression model as a whole is significant?
a. 0.00005 b. 0.001 c. 0.01 d. 0.05
4. Referring to Table 12-4, one individual in the sample had an annual income of $100,000, a family size of 10, and an education of 16 years. This individual owned a home with an area of 7,000 square feet (House = 70.00). What is the residual (in hundreds of square feet) for this data point? a. 7.40 b. 2.52 c. -2.52 d. -5.40
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