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Ordinary least squares (OLS) minimizes the residual sum of squares (RSS) $$RSS=\sum_{i}\left( \varepsilon _{i}\right) ^{2}=\varepsilon ^{\prime }\varepsilon =\sum_{i}\left( y_{i}-\hat{y}_{i}\right) ^{2}$$ The mean squared deviation (in the version you are using it) equals $$MSE=\frac{RSS}{n}$$ where $n$ is the number of observations. Since $n$ is a ...