![]() It is a percentage.Ĭan be used for nonlinear relationships and for relationships that has two or more independent variables. The ratio SSR/SST, which will have a value between 0 and 1, is used to evaluate the goodness of fit for the estimated regression equation, this ratio is called the coefficient of determination (r^2). SSE = SST - SSR, hence the largest value/poorest fit occurs when SSR = 0 and SSE = SST. Poorer fits will result in larger values for SSE. As SST = SSR and SSE, we see that a perfect fit SSR must equal SST and the ratio SSR/SST must equal 1. In this case yi - yi^ would be 0 for each observation resulting in SSE = 0. ![]() The estimated regression equation would give a perfect fit if every value of yi lie on the estimated regression line. ![]() SSE = sum of squares due to error Coefficient of determination, r^2 SST, SSR and SSE can be used to provide a measure of the goodness of fit for the estimated regression equation. Relationship between SST, SSR and SSE SST = SSR + SSE
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