![]() Mean of all of these data points, square them,Īnd just take that sum. The distance between each of these data points and the And you could view itĪs really the numerator when you calculate variance. Want to do in this video is calculate the Through those calculations will give you an The next few videos, we're just really going to beĭoing a bunch of calculations about this data set Thus it tells us how much of the variation in the data is explained by the changing x-values. SSE is the sum of (yhat_i - ybar)^2, so it is the variation of the regression line itself away from the overall mean of the y-values. So it is similar to SSW, it is the residual variation of y-values not explained by the changing x-value. SSR is the sum of (y_i - yhat_i)^2, so it is the variation of the data away from the regression line. SSR (Residuals) + SSE (Explained) = SST (Total) "Treatment" or "Model" (or sometimes "Factor") means the same as "Between groups" This is the variation that IS explained by the fact that there are different groups of data (often because they come from patients who get different treatments). "Error" means the same as "Within groups" This is the variation which is NOT explained by the fact that we can put the data into different groups. ![]() If people use SST to mean "treatment", then they have to write SS(Total) for the total sum of squares, or they might even write TSS for "Total Sum of Squares". Wait, WHAT?! There are two different SST's? I know, it's horrible. But if you search the web or textbooks, you ALSO FIND:Ģ) SSE (Error) + SST (Treatment!!) = SS(Total) THIS IS THE WORST.ģ) SSE (Error) + SSM (Model) = SST (Total) So, in ANOVA, there are THREE DIFFERENT TRADITIONS:ġ) SSW (Within) + SSB (Between) = SST (Total!!) You have to be VERY CAREFUL with these, because depending on the source, you could get confused, especially between Regression and ANOVA.
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