A statistical measure indicating the proportion of variance in the dependent variable that is predictable from the independent variables.
- Range: Typically between 0 and 1.
#Interpretation
- 0: Model explains none of the variability.
- 1: Model explains all the variability.
#Purpose
Assesses goodness-of-fit in regression models; higher values suggest a better fit, though context matters.
#Limitations
- Does not indicate causality.
- Can be misleading with non-linear relationships or overfitting.
- Sensitive to the number of predictors: adding variables may increase R-squared even if they don’t improve predictive power.
#Complementary measures
Adjusted R-squared accounts for model complexity, providing a more accurate assessment in models with multiple predictors.
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