Question

answer both please

0 41 A perfectly vertical scatter plot relationship between Y and X Question 18 (3 points) Since increasing the number of variables increases R2 why not include every variable in the regression equation? 1) Yes, you can include as many significant variables as you wish since computation power is the only limitation on regression modeling. 2) No, since adding more variables will eventually increase the degrees of freedom in the numerator of the F statistic and the F statistic will likely decrease as a result. 3) Yes as long as each variable has a low VIF. U No, since the accuracy of the additional data decreases as more variables are added Question 19 (3 points) If the sum of squared error variation of a variable Y is 28.95 and the sum of squared total variation of the Y data is 290.60 the R square value for the regression is: 1) 119

Answer 1

Answer: Option (2)

Since adding more variables eventually increases the degree of freedom in the numerator of the F statistic and F statistic will likely decreases as a result.

Too many variables in the regression model leads to the following problems.

The number of parameter estimated will be larger than the number of observations which means there is lack of degrees of freedom..

The independent variables will be too correlated

The f-statistic and Adjusted R squared also may not depict a true picture because of high degree of freedom.