square them before we add them up (this idea should be familiar from Section 2.5.2). Therefore, to assess the error in a linear model, just like when we assessed the fit of the mean using the variance, we use a sum of squared errors, and because we call these errors residuals, this total is called the sum of squared residuals or residual sum of squares (SSR). The residual sum of squares is a gauge of how well a linear model fits the data: if the squared differences are large, the model is not representative of the data (there is a lot of error in prediction); if the squared differences are
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