R^2 is the percent of variance explained by the model. A higher R^2 value indicates a better fit.

• A. p-value; smaller is better
• B. percent of variance; higher
• C. coefficient; larger is better
• D. mean error; lower is better

Answer: (B)

R^2 measures how much of the variability in the outcome your model accounts for. It’s the fraction of total variance explained by the model, typically expressed from 0 to 1 (or 0% to 100%). A higher R^2 means the model captures more of the outcome’s variation, so it fits the data better. This differs from p-values, which indicate statistical significance, or from a model coefficient, which describes the relationship’s size and direction but not overall fit. It also differs from mean error, where lower values indicate closer predictions but not the proportion of variance explained. So the statement that R^2 is the percent of variance explained and that higher values indicate a better fit correctly describes what R^2 represents.