Sum of Squares Total (SST)

In regression output...

SST = SSR + SSE
SST = 362018 + 96001
SST = 458019

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

The Sum of Squares Total (SST) sums all the squared-differences between the observed values and the mean of all values.

You may see it referred to as the "total" variance in the data points, as it includes both the "unexplained" variance (due to error) and the "explained" variance (from our regression model).

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.

SST = (-5)2 + (-1)2 + (-4)2 + (+3)2 + (+5)2 + (+2)2
SST = (25) + (1) + (16) + (9) + (25) + (4)
SST = 80

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