365 312 351 3588 question7 44points a real estate

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Question 7 4 / 4 points 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. The business literature involving human capital shows that education influences an individual’s annual income. Combined, these may influence family size. With this in mind, what should the real estate builder be particularly concerned with when analyzing the multiple regression model?
Question 8 4 / 4 points In multiple regression, the __________ procedure permits variables to enter and leave the model at different stages of its development.
Question 9 4 / 4 points A regression diagnostic tool used to study the possible effects of collinearity is
Question 10 4 / 4 points The Variance Inflationary Factor (VIF) measures the correlation of the X variables with the Y variable. correlation of the X variables with each other. contribution of each X variable with the Y variable after all other X variables are included in the model. standard deviation of the slope.
Question 11 4 / 4 points TABLE 15-3 In Hawaii, condemnation proceedings are under way to enable private citizens to own the property that their homes are built on. Until recently, only estates were permitted to own land, and homeowners leased the land from the estate. In order to comply with the new law, a large Hawaiian estate wants to use regression analysis to estimate the fair market value of the land. The following model was fit to data collected for n = 20 properties, 10 of which are located near a cove. where Y = Sale price of property in thousands of dollars X1 = Size of property in thousands of square feet X2 = 1 if property located near cove, 0 if not Using the data collected for the 20 properties, the following partial output obtained from Microsoft Excel is shown: SUMMARY OUTPUT Regression Statistics Multiple R 0.985 R Square 0.970 Standard Error 9.5 Observations 20 ANOVA df SS MS F Signif F Regression 5 28324 5664 62.2 0.0001 Residual 14 1279 91 Total 19 29063 Coeff StdError t Stat P-value Intercept - 32.1 35.7 – 0.90 0.3834
Size 12.2 5.9 2.05 0.0594 Cove – 104.3 53.5 – 1.95 0.0715 Size*Cove 17.0 8.5 1.99 0.0661 SizeSq – 0.3 0.2 – 1.28 0.2204 SizeSq*Cove – 0.3 0.3 – 1.13 0.2749 Referring to Table 15-3, is the overall model statistically adequate at a 0.05 level of significance for predicting sale price ( Y )?

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