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Data & Analytics · Intermediate

Multiple Linear Regression

A regression model that predicts a numeric outcome from two or more predictors: y = β0 + β1x1 + ... + βkxk + ε.

Definition

Each coefficient is the expected change in y per unit change in that predictor, holding the others constant. Assumptions include linearity, independent errors, constant variance (homoscedasticity) and normally distributed residuals. Adjusted R² penalizes adding predictors.

Exam tip

Adding predictors always raises R²; use adjusted R² to compare models.

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