sing 20 observations, the multiple regression model y = Bo + B1x1 + B2x2 + e was stimated. A portion of the regression results is as follows: df MS F Significance F Regression 2 2.15E+12 1.04E+12 60.717 1.81E-08 Residual 17 3.150+11 1.71E+10 Total 19 2.50E+12 SS Intercept X1 X2 Coefficients Standard Error -987,653 131,669 28,979 32,171 31,010 33,496 t Stat -7.501 0.901 0.926 p-Value 0.000 0.380 0.368 Lower 958 -1,265,450 -38, 896 -39,660 Upper 958 -709,856 96,854 101,680 . 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 less than 0.05. O Yes, since the p-value of the appropriate test is more than 0.05. No, since the p-value of the appropriate test is more than 0.05. . 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 cinco hath notice of the annonrista tact are not more than 5 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. O 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. O 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. O Yes, since both p-values of the appropriate test are more than 0.05. O No, since both p-values of the appropriate test are not less than 0.05. O No, since both p-values of the appropriate test are not more than 0.05. c. What is the likely problem with this model? O Multicollinearity since the standard errors are biased. O Multicolinearity since the predictor variables are individually and jointly significant. O Multicollinearity since the predictor variables are individually significant but jointly insignificant Multicollinearity since the predictor variables are individually insignificant but jointly significant