Math — Multiple Regression
Several predictors and the fitted coefficients.
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y = \beta_0 + \beta_1 x_1 + \cdots + \beta_k x_k + \varepsilon
\hat{\boldsymbol{\beta}} = (X^\top X)^{-1}X^\top \mathbf{y}
R^2_{adj} \text{ penalises extra predictors}
\text{watch for multicollinearity}