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Sum of Squares Regression (SSR)

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In regression output...

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SSR = SST - SSE

SSR = SST - SSE
SSR = 458019 - 96001
SSR = 362018

SSR = Mean Square * DF

SSR = MSM * DF
SSR = 362018 * 1
SSR = 362018

Okay... but actually explain it to me now.

Definition

The Sum of Squares Regression (SSR) sums all the squared-differences between the predicted values and the mean of all values.

You may see it referred to as the "explained" variance of the data points that's explained by our regression model.

Scenario

Scenario: Crammer Nation University wants to develop a regression equation to predict the "Number of Recruits" a given fraternity will receive this rush season given the "Parties Thrown" by the fraternity the previous year. They take a sample of 6 fraternities on campus, resulting in the following scatterplot with line of best fit.

Formula

PRO TIP: You may see "expected" instead of "predicted", they mean the same thing!

Visualize mean
Visualize "predicted - mean"
Calculate SSR

SSR = (-3.8)2 + (-3.0)2 + (-1.4)2 + (+1.1)2 + (+2.7)2 + (+4.4)2
SSR = (14.44) + (9.00) + (1.96) + (1.21) + (7.29) + (19.36)
SSR = 53.26

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