www.statology.org/sst-ssr-sse/

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differences between individual data points (yi) and the mean of the response variable (y).

differences between predicted data points (ŷi) and the mean of the response variable(y).

predicted data points (ŷi) and observed data points (yi).

SST = SSR + SSE

we know two of these measures then we can use some simple algebra to calculate the third.

R-squared, sometimes referred to as the coefficient of determination, is a measure of how well a linear regression model fits a datase

. A value of 0 indicates that the response variable cannot be explained by the predictor variable at all

R-squared = SSR / SST

88.14% of the variation in the response variable can be explained by the predictor variab

ine of best fit equation to calculate the predicted exam score () for each student.

Score = 66.615 + 5.0769*(1) = 71.69.

(yi – y)2 = (68 – 81)2 = 169

R-squared = SSR / SST R-squared = 279.23 / 316 R-squared = 0.8836

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