Regression StatisTicsMultiple R0.9161R Square0.8392Adjusted R Square0 8109Standard Error20.1451Observations21ANOVAdfSSMSFRegression336011.50 12003.8329.580.0000Residual176899 00405.82Total2042910.50CoefficientsStd. Errort StatP-valueIntercept37527.360.1370.8927XI3.73O.814.6070.0003X249.7215.693 1690.0056X3-0 910.38-2.4030.0279If we remove any of the 3 predictors from the model the R-Square would decrease Which would cause it to drop themost?O remove X1remove X3.remove X2

Solution The given output is regression output of a regression model computed by Excel.

Multiple R 09161 R Square 0.8392 Adjusted R Square 0 8109 Standard Error 20.1451 Observations 21 ANOVA df SS MS F Regression 3 36011.50 12003.83 29.58 0.0000 Residual 17 6899.00 405 82 Total 20 42910 50 Coefficients Std. Error Stat P-value Intercept 3 75 27.36 0.137 0.8927 XI 3.73 O.S1 4.607 0.0003 X2 49.72 15.69 3 169 0.0056 X3 -0 91 0.38 -2.403 0.0279 If we remove any of the 3 predictors from the model the R-Square would decrease Which would cause it to drop the most? O remove X1 O remove X3 O remove X2

Multiple R 09161 R Square 0.8392 Adjusted R Square 0 8109 Standard Error 20.1451 Observations 21 ANOVA df SS MS F Regression 3 36011.50 12003.83 29.58 0.0000 Residual 17 6899.00 405 82 Total 20 42910 50 Coefficients Std. Error Stat P-value Intercept 3 75 27.36 0.137 0.8927 XI 3.73 O.S1 4.607 0.0003 X2 49.72 15.69 3 169 0.0056 X3 -0 91 0.38 -2.403 0.0279 If we remove any of the 3 predictors from the model the R-Square would decrease Which would cause it to drop the most? O remove X1 O remove X3 O remove X2

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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