Using the lengths (in.), chest sizes (in.), and weights (lb) of bears from a data set, the resulting regression equation is Weightequalsminus274plus0.426 Lengthplus12.1 Chest Size. The P-value is 0.000 and the adjusted Rsquared value is 0.925. If an additional predictor variable of neck size (in.) is included, the P-value becomes 0.000 and the adjusted Rsquared becomes 0.933. Why is it better to use values of adjusted Rsquared instead of simply using values of Rsquared?

a) The unadjusted R^2 decreases or remains the same as more variables are included, but the adjusted R^2 is adjusted for the number of variables and sample size

b) The unadjusted R^2 can only be calculated for regression equations with two or fewer predictor variables while the adjusted R^2 can be calculated for regression equations with any number of predictor variables

c) The unadjusted R^2 increases or remains the same as more variables are included but the unadjusted R^2 is adjusted for the number of variables and sample size

d) The unadjusted R^2 can only be calculated for regression equations with p-values greater than 0 while the adjusted R^2 can be calculated for regression equations with any manner of p

answer : The unadjusted R^2 increases or remains the same as more variables are included but the unadjusted R^2 is adjusted for the number of variables and sample size ( C )

Step-by-step explanation:

As seen the unadjusted R^2 increases anytime a predictor is added to the model even if the predictor has no effect on the model i.e if it is just a chance but the adjusted R^2 has been adjusted for the exact number of predictors in the model and it would increase or decrease based on if the additional predictor added to the model improves the model significantly(more than chance) or if the predictor those not improve the model more than expected