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# ] A partial computer output from a regression analysis using Excel’s Regression tool follows. Regression Statistics...

] A partial computer output from a regression analysis using Excel’s Regression tool follows. Regression Statistics Multiple R (1) R Square 0.923 Adjusted R Square (2) Standard Error 3.35 Observations ANOVA df SS MS F Significance F Regression (3) 1612 (7) (9) Residual 12 (5) (8) Total (4) (6) Coefficients Standard Error t Stat P-value Intercept 8.103 2.667 x1 7.602 2.105 (10) x2 3.111 0.613 (11)

The multiple correlation coefficient is 0.92187417. This indicates that the correlation among the independent and dependent variables is positive. This statistic, which ranges from -1 to +1, does not indicate statistical significance of this correlation. 2. The coefficient of determination, R2 , is 84.99%. This means that close to 85% of the variation in the dependent variable (home prices) is explained by the independent variables. 3. The adjusted R-square, a measure of explanatory power, is 0.82795539. This statistic is not generally interpreted because it is neither a percentage (like the R2 ), nor a test of significance (such as the Fstatistic). 4. The standard error of the regression is \$419,334, which is an estimate of the variation of the observed home prices, in dollar terms, about the regression

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