Using 20 observations, the multiple regression model y=+1x1+2x2+ was estimated. A portion of the regression results is as follows: a. At the 5% significance level, are the predictor variables jointly significant? No, since the p-value of the appropriate test is less than 0.05 . Yes, since the p-value of the appropriate test is more than 0.05 . Yes, since the p-value of the appropriate test is less than 0.05 . No, since the p-value of the appropriate test is more than 0.05 . b. At the 5% significance level, is each predictor variable individually significant? Yes, since both p-values of the appropriate test are less than 0.05 . Yes, since both p-values of the appropriate test are more than 0.05 . No, since both p-values of the appropriate test are not less than 0.05 . No, since both p-values of the appropriate test are not more than 0.05 . b. At the 5% significance level, is each predictor variable individually significant? Yes, since both p-values of the appropriate test are less than 0.05 . Yes, since both p-values of the appropriate test are more than 0.05 . No, since both p-values of the appropriate test are not less than 0.05 . No, since both p-values of the appropriate test are not more than 0.05 . c. What is the likely problem with this model? Multicollinearity since the standard errors are biased. Multicollinearity since the predictor variables are individually and jointly significant. Multicollinearity since the predictor variables are individually significant but jointly insignificant. Multicollinearity since the predictor variables are individually insignificant but jointly significant