True or false the regression sum of squares ssr can

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Question 154 / 4 pointsTrue or False: The Regression Sum of Squares (SSR) can never be greater than the Total Sum of Squares (SST).
Question 164 / 4 pointsTrue or False: The coefficient of determination represents the ratio of SSR to SST.
Question 174 / 4 pointsIn a multiple regression problem involving two independent variables, if b1is computed to be +2.0, it means that
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Question 184 / 4 pointsThe coefficient of multiple determination rY.12measures the variation around the predicted regression equation.measures the proportion of variation in Ythat is explained by X1and Xmeasures the proportion of variation in Ythat is explained by X1holding X2constant.will have the same sign as b1
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.Question 194 / 4 pointsThe variation attributable to factors other than the relationship between the independent variables and the explained variable in a regression analysis is represented by
Question 204 / 4 points
TABLE 14-3An 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 OUTPUTRegression StatisticsMultiple R  0.991R Square  0.982Adjusted R Square 0.976Standard Error  0.299Observations  10ANOVA             df      SS          MS           F        Signif FRegsion  2    33.4163   16.7082    186.325    0.0001Resdual  7     0.6277    0.0897Total     9   34.0440                Coeff     StdError   t Stat    P-valueIntcept   – 0.0861     0.5674     – 0.152    0.8837GDP          0.7654     0.0574      13.340    0.0001Price      – 0.0006     0.0028     – 0.219     0.8330Referring 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 r2value 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? 

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