Using 20 observations, the multiple regression model y a 1x12x2 + was estimated. A portion of the regression results is as follows: 55 2. 14.12 3. 15:11 2.38E-12 Regression Residual Total 65.620 of 2 17 19 MS 1.11.12 1.69E10 Significance 1.013-8 Intercept X2 *2 Coefficients -987, 165 28,359 29,348 Standard Error 132,064 32,306 31,945 Stat -7.475 0.878 0.919 -Value w.ece 0.392 0.371 Lower 95 -1,265,796 --39,601 -38,650 Upper 959 -708,534 96,519 96,746 a. At the 5% significance level, are the predictor variables jointly significant? 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. Yes, since the p value of the appropriate test is more than 0.05. No, since the p-value of the appropriate test is less 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 blased. Multicollinearity since the predictor variables are individually and jointly significant Multicolinearlty since the predictor variables are individually significant but jointly insignificat. Multicolinearity since the predictor variables are individually Insignificant but jointly significant Using 20 observations, the multiple regression modely - Bo B1x1 Byx2 + was estimated. A portion of the regression results is as follows: d1 SS MS Significance Regression 2 2.14E+12 1.11E+12 65.620 1.91E-08 Residual 17 3. 15E+11 1.69E+10 Total 19 2.38E+12 Intercept X1 *2 Coefficients -987, 165 28,359 29,348 Standard Error 132,064 32,306 31.945 stat -7.475 8.878 0.919 p-Value 0.000 0.392 0.371 Lower 959 -1,265,796 -39,801 -38,058 Upper 954 -708,534 96,519 96,746 a. At the 5% significance level, are the predictor variables jointly significant? 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. Yes, since the p-value of the appropriate test is more than 0.05. No, since the p-value of the appropriate test is less than 0.05. b. At the 5% significance level, Is each predictor variable Individually significant? Yes, since both p-values of the appropriate testare 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? Multicolinearity since the standard errors are based Multicollineorty since the predictor variables are individually and jointly significant Multicolinearity since the predictor variables are individually significant but jointy insignificant Multicolinearity since the predictor variables are Individually insignificant but jointly significant