Yao Yao on September 29, 2014

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## The Residual Sum of Squares (RSS) is the sum of the squared residuals

• RSS: Residual Sum of Squares
• SSR: Sum of Squared Residuals
• SSE: Sum of Squared Errors

## The Residual Standard Error (RSE) is the square root of $\frac{RSS}{\text{degrees of freedom}}$

where

• $p$ is the number of predictors
• i.e. $p+1$ is the number of right-hand-side variables, including the intercept, in a regression model
• $m-p-1$ denotes the degrees of freedom.

where $\bar y$ is the sample mean.

Further we have $Var = \frac{TSS}{m - 1}$

## Chain Reaction

• $R^2$ ↑
• Adjusted $R^2$ 不好说（这正是 adjustment 的体现）