Final answer:
Adjusted R² accounts for the number of predictor variables in a statistical model, adjusting the R² to provide a more accurate measure when multiple predictors are involved.
Step-by-step explanation:
In order to avoid the possibility of R² creating a false impression, virtually all software packages include adjusted R². Unlike R², the adjusted R² explicitly accounts for the number of predictor variables (k) in addition to sample size (n), which modifies the statistic to penalize for adding variables that do not improve the model significantly.
The R² value represents the percentage of variation in the dependent variable that can be explained by variation in the independent variables using the best-fit regression line. However, it does not consider the complexity of the model, which might include non-contributing predictors. Adjusted R² adjusts for the total number of variables included in the model relative to the number of data points thereby providing a more accurate measure of the strength of association when multiple predictors are used.