Question

] 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)

Answer 1

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