Multiple Linear Regression (cross-cutting/numerics/multiple-linear-regression)

Use when you must fit a multiple linear regression model: solve the normal equations for the partial-regression-coefficient vector from two or more predictors, compute each coefficient-standard-error with the two-sided p-value of its t statistic, the overall regression-f-test p-value, R-squared and adjusted-r-squared, and the variance-inflation-factor multicollinearity-check for every predictor, then predict the response at a new design point. Produces the coefficient vector, diagnostic table and prediction that gate whether the linear model explains the data. Trigger: multiple-linear-regression, variance-inflation-factor, adjusted-r-squared, partial-regression-coefficien…