Question

4) A high r squared or adjusted r squared does not mean

a. The included variables are statistically significant.

b. That you have an unbiased estimator.

c. That you have an appropriate set of regressors.

d. all of the above.

5) The adjusted r squared is different from the standard r squared in that it

a. is always higher

b. accounts for correlation between the error term, and the regressors

c. corrects for degrees of freedom (number of regressors included in the model)

d. can determine if the coefficients are biased

Answer 1

Ans 4 the correct ans is D all of the above

R-squared is a goodness-of-fit measure for linear regression models. This statistic indicates the percentage of the variance in the dependent variable that the independent variables explain collectively.You cannot use R-squared to determine whether the coefficient estimates and predictions are biased, which is why you must assess the residual plots.Residual plots can expose a biased model far more effectively than the numeric output by displaying problematic patterns in the residuals.R-squared does not indicate if a regression model provides an adequate fit to your data. A good model can have a low R2 value. On the other hand, a biased model can have a high R2 value!

Ans 5 The correct ans is C

"Adjusted R Square" takes into account the number of explanatory variables (Xs) and the sample size, i.e., it is adjusted based on the df.As explanatory variables are added to the model, each one (the X's) will explain some of the variance in the dependent variable (Y) simply due to chance.As a matter of fact, R Square will always increase when you add more predictors. However, some of this increase in R Square would be simply due to chance variation in that particular sample and not necessarily because the model is better. The adjusted R Square attempts to yield a more honest value to estimate the R Squared for the population