Question 14 (2 points) all What is the name of the estimation technique used to calculate simple regression equations? 0 Least Observed Determinant 0 Ordinary Least Squares 0 Optimal Effective Estimate 0 Maximum Error Output Question 15 (2 points) () Listen A multiple regression is conducted with Y as the dependent variable and X1, X2, X3, and X4 as the explanatory variables. The Excel output for this regression is shown below: SUMMARY OUTPUT Regression Statistics Multiple R 0.7236 R Square 0.5236 Adjusted R Square 0.5159 Standard Error 5.3928 Observations 252 ANOVA df SS MS F Significance F Regression 4 7895.757 1973.939 67.875 1.10662E-38 Residual 247 7183.260 29.082 Total 251 15079.017 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 17.7278 7.9594 2.2273 0.026831 2.0508 33.4049 X1 0. 1558 0.0275 5.6662 4.05E-08 0. 1017 0.2100 X2 0. 1838 0.0200 9. 1999 1.56E-17 0. 1444 0.2231 X3 -0.5510 0.0996 -5.5348 7.94E-08 -0.7471 -0.3549 X4 -0.0003 0. 1888 -0.0016 0.998688 -0.3721 0.3715 What is the value of R-Square? ScreenshotANOVA df SS MS F Significance F Regression 4 7895.757 1973.939 67.875 1.10662E-38 Residual 247 7183.260 29.082 Total 251 15079.017 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 17.7278 7.9594 2.2273 0.026831 2.0508 33.4049 X1 0. 1558 0.0275 5.6662 4.05E-08 0. 1017 0.2100 X2 0. 1838 0.0200 9. 1999 1.56E-17 0.1444 0.2231 X3 -0.5510 0.0996 -5.5348 7.94E-08 -0.7471 -0.3549 X4 -0.0003 0. 1888 -0.0016 0.998688 -0.3721 0.3715 What is the value of R-Square? 0.7236 0.5236 0.5159 5.3928 Question 16 (2 points) ScreenshotQuestion 16 (2 points) Listen A multiple regression is conducted with Y as the dependent variable and X1, X2, X3, and X4 as the explanatory variables. The Excel output for this regression is shown below: SUMMARY OUTPUT Regression Statistics Multiple R 0.7236 R Square 0.5236 Adjusted R Square 0.5159 Standard Error 5.3928 Observations 252 ANOVA df SS MS F Significance F Regression 4 7895.757 1973.939 67.875 1.10662E-38 Residual 247 7183.260 29.082 Total 251 15079.017 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 17.7278 7.9594 2.2273 0.026831 2.0508 33.4049 X1 0. 1558 0.0275 5.6662 4.05E-08 0. 1017 0.2100 X2 0. 1838 0.0200 9. 1999 1.56E-17 0.1444 0.2231 X3 -0.5510 0.0996 -5.5348 7.94E-08 -0.7471 -0.3549 X4 -0.0003 0. 1888 -0.0016 0.998688 -0.3721 0.3715 What is the value of the standard error of the model? ScreenshotOldllualu CITUI Observations 252 ANOVA df SS MS F Significance F Regression 4 7895.757 1973.939 67.875 1.10662E-38 Residual 247 7183.260 29.082 Total 251 15079.017 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 17.7278 7.9594 2.2273 0.026831 2.0508 33.4049 X1 0.1558 0.0275 5.6662 4.05E-08 0. 1017 0.2100 X2 0. 1838 0.0200 9. 1999 1.56E-17 0.1444 0.2231 X3 -0.5510 0.0996 -5.5348 7.94E-08 -0.7471 -0.3549 X4 -0.0003 0. 1888 -0.0016 0.998688 -0.3721 0.3715 What is the value of the standard error of the model? 5.3928 7.9594 0.0275 2.2273 ScreenshotQuestion 17 (2 points) Listen A multiple regression is conducted with Y as the dependent variable and X1, X2, X3, and X4 as the explanatory variables. The Excel output for this regression is shown below: SUMMARY OUTPUT Regression Statistics Multiple R 0.7236 R Square 0.5236 Adjusted R Square 0.5159 Standard Error 5.3928 Observations 252 ANOVA df SS MS F Significance F Regression 4 7895.757 1973.939 67.875 1.10662E-38 Residual 247 7183.260 29.082 Total 251 15079.017 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 17.7278 7.9594 2.2273 0.026831 2.0508 33.4049 X1 0. 1558 0.0275 5.6662 4.05E-08 0. 1017 0.2100 X2 0. 1838 0.0200 9. 1999 1.56E-17 0. 1444 0.2231 X3 -0.5510 0.0996 -5.5348 7.94E-08 -0.7471 -0.3549 X4 -0.0003 0. 1888 -0.0016 0.998688 -0.3721 0.3715 What is the value of the slope for variable X2? ScreenshotANOVA df SS MS F Significance F Regression 4 7895.757 1973.939 67.875 1.10662E-38 Residual 247 7183.260 29.082 Total 251 15079.017 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 17.7278 7.9594 2.2273 0.026831 2.0508 33.4049 X1 0. 1558 0.0275 5.6662 4.05E-08 0. 1017 0.2100 X2 0. 1838 0.0200 9.1999 1.56E-17 0.1444 0.2231 X3 -0.5510 0.0996 -5.5348 7.94E-08 -0.7471 -0.3549 X4 -0.0003 0. 1888 -0.0016 0.998688 -0.3721 0.3715 What is the value of the slope for variable X2? -0.5510 0.1558 0.1838 9.1999 Question 18 (2 points) Screenshot ListenQuestion 18 (2 points) ) Listen A multiple regression is conducted with Y as the dependent variable and X1, X2, X3, and X4 as the explanatory variables. The Excel output for this regression is shown below: SUMMARY OUTPUT Regression Statistics Multiple R 0.7236 R Square 0.5236 Adjusted R Square 0.5159 Standard Error 5.3928 Observations 252 ANOVA df SS MS F Significance F Regression 4 7895.757 1973.939 67.875 1.10662E-38 Residual 247 7183.260 29.082 Total 251 15079.017 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 17.7278 7.9594 2.2273 0.026831 2.0508 33.4049 X1 0. 1558 0.0275 5.6662 4.05E-08 0. 1017 0.2100 X2 0. 1838 0.0200 9. 1999 1.56E-17 0. 1444 0.2231 X3 -0.5510 0.0996 -5.5348 7.94E-08 -0.7471 -0.3549 X4 -0.0003 0. 1888 -0.0016 0.998688 -0.3721 0.3715 Which variables are important in predicting Y at the 0.05 level? ScreenshotSS MS F Significance F Regression 7895.757 1973.939 67.875 1.10662E-38 Residual 247 7183.260 29.082 Total 251 15079.017 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 17.7278 7.9594 2.2273 0.026831 2.0508 33.4049 X1 0. 1558 0.0275 5.6662 4.05E-08 0. 1017 0.2100 X2 0. 1838 0.0200 9. 1999 1.56E-17 0.1444 0.2231 X3 -0.5510 0.0996 -5.5348 7.94E-08 -0.7471 -0.3549 X4 -0.0003 0. 1888 -0.0016 0.998688 -0.3721 0.3715 Which variables are important in predicting Y at the 0.05 level? All the variables (X1, X2, X3, and X4) are important OX1, X2 and X3 O X1, X3 and X4 O Only X4 Question 19 (2 points) E () Listen Screenshot multiple regres ducted with Y as the dependent variable and X1 X2 x3Question 19 (2 points) Listen A multiple regression is conducted with Y as the dependent variable and X1, X2, X3, and X4 as the explanatory variables. The Excel output for this regression is shown below: SUMMARY OUTPUT Regression Statistics Multiple R 0.7236 R Square 0.5236 Adjusted R Square 0.5159 Standard Error 5.3928 Observations 252 ANOVA df SS MS F Significance F Regression 4 7895.757 1973.939 67.875 1.10662E-38 Residual 247 7183.260 29.082 Total 251 15079.017 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 17.7278 7.9594 2.2273 0.026831 2.0508 33.4049 X1 0. 1558 0.0275 5.6662 4.05E-08 0. 1017 0.2100 X2 0.1838 0.0200 9. 1999 1.56E-17 0. 1444 0.2231 X3 -0.5510 0.0996 -5.5348 7.94E-08 -0.7471 -0.3549 X4 -0.0003 0. 1888 -0.0016 0.998688 -0.3721 0.3715 Why is this a good model at the 0.05 level? ScreenshotResidual 247 7183.260 29.082 Total 251 15079.017 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 17.7278 7.9594 2.2273 0.026831 2.0508 33.4049 X1 0. 1558 0.0275 5.6662 4.05E-08 0.1017 0.2100 X2 0.1838 0.0200 9. 1999 1.56E-17 0. 1444 0.2231 X3 -0.5510 0.0996 -5.5348 7.94E-08 -0.7471 -0.3549 X4 -0.0003 0. 1888 -0.0016 0.998688 -0.3721 0.3715 Why is this a good model at the 0.05 level? Because the sum of SSR and SSE equals SST. O The p-value of the F-statistic is below 0.05. O The p-value on the F-statistic is above 0.05. The variable X4 has a p-value of 0.998688. Question 20 (2 points) Listen A researcher wants to regress a dependent variable, Y, on two explanatory variab Screenshot (X1 and X2) but believes there may be nonlinearity issues. She decides to find theQuestion 20 (2 points) () Listen A researcher wants to regress a dependent variable, Y, on two explanatory variable (X1 and X2) but believes there may be nonlinearity issues. She decides to find the reciprocal of each explanatory variable (call them RECX1 and RECX2) and the natural log of each (call them LNX1 and LNX2). Using the correlation matrix provided below, what forms should X1 and X2 take when estimating the best multiple regression model? Y X1 X2 RECX1 LNX1 RECX2 LNX2 Y 1 X1 0.6132 X2 -0.0891 0.3083 RECX1 -0.6325 -0.9642 -0.3313 LNX1 0.6296 0.9893 0.3235 -0.9923 RECX2 0. 1045 -0. 1391 -0.9248 0. 1503 -0. 1460 LNX2 -0.0987 0.2271 0.9806 -0.2447 0.2385 -0.9814 OLNX1 and LNX2 RECX1 and RECX2 OLNX1 and RECX2 O RECX1 and LNX2 Screenshot