Multivariate Regression

Also known as: Multiple Regression, OLS, Driver Analysis

Extension of regression including multiple predictor variables to understand which factors best explain a target variable.

Multivariate Regression (Multiple Regression) is an extension of simple regression that includes two or more predictor (independent) variables to explain or predict a dependent variable. It allows quantifying the unique effect of each predictor while controlling for the effect of all others.

In market research, it is the standard technique for Driver Analysis: which of the 15 product attributes evaluated in the survey have the greatest impact on overall satisfaction, when controlling for the effects of all others? Standardized beta coefficients indicate the relative importance of each driver.

Important assumptions: linearity, error independence, homoscedasticity, and no multicollinearity between predictors. When these assumptions are violated, alternatives include ridge regression, elastic net, or tree-based models.