Multiple Linear Regression Explained Coefficients Assumptions Multicollinearity and Model Diagnostics
Multiple linear regression estimates how an outcome changes with several predictors while holding the other included variables constant. This practical guide explains coefficients, dummy variables, interactions, R-squared, residuals, multicollinearity, variance inflation factors, influential observations, prediction, common mistakes and a credit-risk example without inventing data.