A multiple regression analysis produced the following output from Minitab.Regression Analysis: Y versus x and xPredictor Coef SE Coef T PConstant -0.0626 0.2034 -0.31 0.762x 1.1003 0.5441 2.02 0.058x -0.8960 0.5548 -1.61 0.124S = 0.179449 R-Sq = 89.0% R-Sq(adj) = 87.8%Analysis of VarianceSource DF SS MS F PRegression 2 4.7013 2.3506 73.00 0.000ResidualError18 0.5796 0.0322Total 20 5.2809These results indicate that____________ A multiple regression analysis produced the following output from Minitab.Regression Analysis: Y versus X₁ and X2Predictor CoefConstant -0.0626x1x21.1003Analysis of VarianceTotalSE Coef T0.2034DF0.5441SourceRegression 2Residual 18 0.5796ErrorSS-0.8960 0.5548 -1.61 0.124S = 0.179449 R-Sq = 89.0% R-Sq(adj) = 87.8%4.7013-0.3120 5.28092.02These results indicate thatPMS0.762F0.058P2.3506 73.00 0.0000.0322at least one of the variable is significant at 5% leveleach predictor variable is significant at the 5% levelX₂ is the only predictor variable significant at the 5% levelnone of the predictor variables are significant at the 5% levelx₁ is the only predictor variable significant at the 5% level

P-value approach of hypothesis testing: The p-value is calculated using the test statistic (t-test or z-test) and the computed p-value is compared with the level of significance. If the p-value is greater than the level of significance then the slope coefficient is said to be not significant. To test the significance of each variable For predictor variable x1 define: H0: β1=0 H1:β1≠0 Level of significance =0.05 Calculated p-value =0.058 the p-value is greater than 0.05 so this slope coefficient is insignificant. Similarly For predictor variable x2 define: H0: β2=0 H1:β2≠0 Level of significance =0.05 Calculated p-value =0.124 the p-value is greater than 0.05 so this slope coefficient is insignificant. So we can conclude that none of the predictor variables is significant at the 5% level.