Mean square regression is a statistical measure used to assess the goodness of fit of a regression model by comparing the variability explained by the model to the total variability in the data. It is calculated as the ratio of the regression sum of squares to its degrees of freedom, and provides insights into how well the independent variables explain the dependent variable's variance.
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