# Question 18 4 4 points the coefficient of multiple

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Question 18 4 / 4 points The coefficient of multiple determination r 2 Y .12 measures the variation around the predicted regression equation. measures the proportion of variation in Y that is explained by X 1 and X measures the proportion of variation in Y that is explained by X 1 holding X 2 constant. will have the same sign as b 1 2 . .
Question 19 4 / 4 points The variation attributable to factors other than the relationship between the independent variables and the explained variable in a regression analysis is represented by
Question 20 4 / 4 points TABLE 14-3 An economist is interested to see how consumption for an economy (in \$ billions) is influenced by gross domestic product (\$ billions) and aggregate price (consumer price index). The Microsoft Excel output of this regression is partially reproduced below. SUMMARY OUTPUT Regression Statistics Multiple R 0.991 R Square 0.982 Adjusted R Square 0.976 Standard Error 0.299 Observations 10 ANOVA df SS MS F Signif F Regsion 2 33.4163 16.7082 186.325 0.0001 Resdual 7 0.6277 0.0897 Total 9 34.0440
Coeff StdError t Stat P-value Intcept – 0.0861 0.5674 – 0.152 0.8837 GDP 0.7654 0.0574 13.340 0.0001 Price – 0.0006 0.0028 – 0.219 0.8330 Referring to Table 14-3, when the economist used a simple linear regression model with consumption as the dependent variable and GDP as the independent variable, he obtained an r 2 value of 0.971. What additional percentage of the total variation of consumption has been explained by including aggregate prices in the multiple regression? In other words, the economist was explaining 97.1%, how much has that percentage or R-square increased after adding "price" as a second independent variable?
Question 21 4 / 4 points TABLE 14-3 An economist is interested to see how consumption for an economy (in \$ billions) is influenced by gross domestic product (\$ billions) and aggregate price (consumer price index). The Microsoft Excel output of this regression is partially reproduced below. SUMMARY OUTPUT Regression Statistics Multiple R 0.991 R Square 0.982 Adjusted R Square 0.976 Standard Error 0.299 Observations 10 ANOVA df SS MS F Signif F Regsion 2 33.4163 16.7082 186.325 0.0001 Resdual 7 0.6277 0.0897 Total 9 34.0440 Coeff StdError t Stat P-value Intcept – 0.0861 0.5674 – 0.152 0.8837 GDP 0.7654 0.0574 13.340 0.0001 Price – 0.0006 0.0028 – 0.219 0.8330 1. Referring to Table 14-3, the p -value for GDP is