The following step-by-step example shows how to calculate each of these metrics for a given regression model in Excel. Sum of Squares Error (SSE) – The sum of squared differences between predicted data points (ŷ i) and observed data points (y i). Sum of Squares Regression (SSR) – The sum of squared differences between predicted data points (ŷ i) and the mean of the response variable( y).ģ. Sum of Squares Total (SST) – The sum of squared differences between individual data points (y i) and the mean of the response variable ( y).Ģ. We often use three different sum of squares values to measure how well a regression line actually fits a dataset:ġ.
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