A study was conducted on 64 female college athletes. The researcher collected da

Question

A study was conducted on 64 female college athletes. The researcher collected data on a number of variables including percent body fat, total body weight, height, and age of athlete. The researcher wondered if % body at %BF height HGT), and/or age are significant predictors of total body weight. All oond tions have been checked and are met and no transformations were needed. The technology output from the multiple regression analysis is given below. If age was removed from the model, what would happen to R-square? Xi Click the icon to view the multiple regression analysis 1Multiple Regression Analysis Choose the correct answer below. Predicto Constant %BF HGT AGE Coef SE Coef ( A. R-square would go down or stay the same, but would not go up ( B. R-square would go up since age was not a significant predictor of total body weight. ° C. R-square would stay the same since age was not a significant predictor of total body weight. 0 D. More information is needed to determine what would happen to R-square if age was removed -97.69 28.79 3.39 0.001 4.36 0.000 9.320.000 0.144 1.3643 0.3126 3.4285 0.3679 -0.9601 0.6483-1.48 S= 10.1086 R-Sq=66.9% Analysis of Variance Source DF MS Residual Error Total 3 12,407.9 4136.0 40.48 0.000 60 6131.0 .2 63 18,539.0 102.2 Click to select your answer

Explanation / Answer

If R-square would down or stay the same, but will not go up.

Upon removing co-variates / independent variables, you can expect the Rsquare to dip , since sum of squared error would increase, as

the OLS regression will have 'more way to wiggle'

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