Regression analysis examines relationships between variables for understanding and prediction.

Simple Regression models Y = a + bX + error, with slope showing change in Y per unit X.

Multiple Regression includes several predictors, showing effects while controlling for others.

Model Evaluation uses R-squared, F-tests, and residual analysis.

Interpretation requires distinguishing association from causation and statistical from practical significance.