Multiple Linear Regression – Higher Dimensions, Test Assumptions, Interpretation of Outputs

Multiple Linear Regression explains how to model and predict outcomes using multiple variables, calculate and interpret coefficients, test model and variable significance, evaluate adjusted R², and validate assumptions including linearity, multicollinearity, normality, homoscedasticity, and residual independence.