![]() It is the sum of the square of the difference between the predicted value and mean of the value of all the data points. Regression SS is the total variation in the dependent variable that is explained by the regression model. Sum of Squares (SS) Regression line with the mean of the dataset in red. Total df - is the sum of the regression and residual degrees of freedom, which equals the size of the dataset minus 1. In this example, both the GRE score coefficient and the constant are estimated. Residual df is the total number of observations (rows) of the dataset subtracted by the number of variables being estimated. Since we only consider GRE scores in this example, it is 1. Regression df is the number of independent variables in our regression model.
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