Question

You create a model using HSPercentile, Gender, and SAT Score to predict CombinedScore. You want to evaluate the model using the Adjusted R2 value. You obtain a value of 0.76. How should you explain this to Sydney?

a. The regression model with HSPercentile, Gender and SAT Score explains 76% of the variability in CombinedScore, adjusting for the number of predictors.
b. Not enough information.
c. Holding all other variables constant, a 1 unit change in the predictor variables results in a 0.76 increase in CombinedScore
d. The regression model with CombinedScoreexplains 76% of the variability in HSPercentile, Gender and SAT Score

Answer 1

a. The regression model with HSPercentile, Gender, and SAT Score explains 76% of the variability in CombinedScore, adjusting for the number of predictors.

Step-by-step explanation:

The Adjusted R2 value measures the proportion of variability in the dependent variable (CombinedScore) that is explained by the independent variables (HSPercentile, Gender, and SAT Score) in the regression model, taking into account the number of predictors. In this case, an Adjusted R2 value of 0.76 indicates that the model with HSPercentile, Gender, and SAT Score explains 76% of the variability in CombinedScore while adjusting for the number of predictors. This suggests that these three variables together have a strong relationship with the predicted CombinedScore.