SUMMARY OUTPUT Regression Statistics Multiple R 0.8687 R Square 0.7547 Adjusted R Square 0.7459 Standard Error 4.6853 Observations 30 ANOVA df Regression Residual Total Intercept Class1 Midterm estimate 1 28 29 SS MS F Significance F 1890.6159 1890.6159 86.1266 0.0000 614.6445 21.9516 2505.2604 Coefficients Standard Error 19.5566 2.7992 1.4054 0.1514 t Stat P-value 6.9865 0.0000 9.2804 0.0000 Lower 95% Upper 95% 13.8227 25.2905 1.0952 1.7156 54.69 Use regression analysis to compute the linear regression equation for the data with the final exam for Class1 as the dependent variable and the scores on the midterm for Class1 as the independent variable. If someone made a 25 on the midterm in Class1, what would be your estimate of their grade on the final exam in Class1? The regression equation is Y = 19.5566 + 1.4054 x Making a 25 on the midterm in Class 1 would give an estimate of making a 54.69 on the final. Class 1Final Var=80.55 Class2Final Assignment 28.75 24.05 61.25 50.05 42.5 35.05 53.75 44.05 53.75 44.05 36.25 30.05 38.75 32.05 37.5 31.05 45 37.05 41.25 34.05 48.75 40.05 41.25 34.05 30 25.05 45 37.05 36.25 30.05 53.75 44.05 62.5 51.05 50 41.05 52.5 43.05 43.75 36.05 38.75 32.05 60 49.05 52.5 43.05 46.25 38.05 50 41.05 37.5 31.05 31.25 26.05 41.25 34.05 37.5 31.05 31.25 26.05 Var=55.29 Class1 Midterm 15 11 15 28 13 19 20 26 20 22 18 13 20 10 10 11 13 16 16 15 19 16 20 16 10 12 12 22 10 13 15 23 12 28 14 20 11 20 13 15 15 10 19 30 13 16 13 24 18 20 10 16 20 10 11 18 12 13 14 15 35 30 f(x) = 0.5369701254x - 6.1833018025 25 20 Class 1 Midterm 15 10 5 0 25 30 35 40 45 50 55 60 65 Class 1 Final The regression equation is Y = 19.5566 + 1.4054 x Making a 25 on the midterm in Class 1 would give an estim SUMMARY OUTPUT Regression Statistics Multiple R 0.8687 R Square 0.7547 Adjusted R Square 0.7459 Standard Error 4.6853 Observations 30 ANOVA df Regression Residual Total Intercept Class1 Midterm 1 28 29 SS 1890.6159 614.6445 2505.2604 Coefficients Standard Error 19.5566 2.7992 1.4054 0.1514 MS 1890.6159 21.9516 F Significance F 86.1266 0.0000 t Stat P-value Lower 95% Upper 95% 6.9865 0.0000 13.8227 25.2905 9.2804 0.0000 1.0952 1.7156 estimate 54.69 60 65 s Y = 19.5566 + 1.4054 x erm in Class 1 would give an estimate of making a 54.69 on the final. Upper 95% Class 1Final Class2Final Assignment Class1 Midterm 28.75 24.05 15 11 61.25 50.05 15 28 42.5 35.05 13 19 53.75 44.05 20 26 53.75 44.05 20 22 36.25 30.05 18 13 38.75 32.05 20 10 37.5 31.05 10 11 45 37.05 13 16 41.25 34.05 16 15 48.75 40.05 19 16 41.25 34.05 20 16 30 25.05 10 12 45 37.05 12 22 36.25 30.05 10 13 53.75 44.05 15 23 62.5 51.05 12 28 50 41.05 14 20 52.5 43.05 11 20 43.75 36.05 13 15 38.75 32.05 15 10 60 49.05 19 30 52.5 43.05 13 16 46.25 38.05 13 24 50 41.05 18 20 37.5 31.05 10 16 31.25 26.05 20 10 41.25 34.05 11 18 37.5 31.05 12 13 31.25 26.05 14 15 Var=80.55 Var=55.29 SUMMARY OUTPUT Regression Statistics Multiple R 1 R Square 1 Adjusted R Square 1 Standard Error 6.00240E-015 Observations 30 ANOVA df Regression Residual Total 1 28 29 Intercept Class2Final Coefficients -1.3125 1.25 Prediction Class2Final 25 SS MS F Significance F 2505.260416667 2505.2604167 6.9535E+031 0 1.0088052E-027 3.602876E-029 2505.260416667 Standard Error t Stat 5.5776521E-015 -2.35314E+014 1.4990227E-016 8.33877E+015 Class1Final 29.9375 P-value 0 0 Lower 95% Upper 95% Lower 95.0% Upper 95.0% -1.3125 -1.3125 -1.3125 -1.3125 1.25 1.25 1.25 1.25 \f