Below is a multiple regression in which the dependent variable is market value of houses and the independent variables are the age of the house and square footage of the house. The regression was estimated for 42 houses.
Adjusted R Square0.532981
Regression2 25376501711.27E+09 24.39544 1.3443E-07
Total 41 4566069762
Coefficients Standard t Stat P-value Lower 95% Upper 95%
Intercept 47331.38 13884.34664 3.408974 0.001528 19247.6673 75415.0958
House Age -825.161 607.3128421 -1.35871 0.182046 -2053.5662 403.243744
Square Feet40.911076.6965239946.109299 3.65E-07 27.3660835 54.4560534
60) Refer to Scenario 2. What percentage of the variation in the dependent variable, Market Value, is explained by the regression model?
61) Refer to Scenario 2. If the age of a house increases by 1 year given that the square feet is held constant, what is the impact on the house's market value?
62) Refer to Scenario 2. By examining the t-statistics associated with the regression coefficients, at the 5 percent significance level, which of the two independent variables are statistically different from zero?
63) Refer to Scenario 2. Based on the 95 percent confidence intervals for each of the partial regression coefficients, which independent variable is statistically different from zero and why?
64) Refer to Scenario 2. What are the units of measurement for the standard error of the estimate?
65) Elaborate on the statement "Every multiple regression analysis is influenced by the sample of the data used."
66) Briefly explain why empirical consumer demand studies such as Patrick McCarthy's study of automobile demand are relevant to managers.
67) Why are estimated models of demand and consumer behavior useful to managers?
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