. Assume you ran a multiple regression to gain a better understanding of the rel
ID: 3054610 • Letter: #
Question
. Assume you ran a multiple regression to gain a better understanding of the relationship between lumber sales, housing starts, and commercial construction. The regression uses lumber sales (in $100,000s) as the response variable with housing starts (in 1000s)(in 1,000s) and commercial construction (in 1000s)(in 1,000s) as the explanatory variables. The estimated model is: Lumber Sales = ?0 + ?1*Housing Starts + ?2*Commercial Constructions + ?. The following ANOVA table summarizes a portion of the regression results. df SS MS F Regression 2 180,770 90,385 103.3 Residual 45 39,375 875 Total 47 220,145 Coefficients Standard Error t-stat p-value Intercept 5.37 1.71 3.14 0.003 Housing Starts 0.76 0.09 8.44 0 Commercial Construction 1.25 0.33 3.78 0.0005 The explanatory variables (Housing Starts and Commercial Construction) together explained approximately _____% of the variations in the response variable (Lumber Sales). a. 18 b. 22 c. 78 d. 82
Explanation / Answer
here from above coefficient of determination =SSR/SST =180770/220145=0.8211
therefore The explanatory variables (Housing Starts and Commercial Construction) together explained approximately
=R2*100 =82% of the variations in the response variable (Lumber Sales).
option D is correct
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