Once a regression model has been fit to a set of data, three measures of variation determine how much of the variation in the dependent variable Y can be explained by variation in the independent variable X. The first measure, the total sum of squares (SST), is a measure of variation of the Y values around their mean, . In a regression analysis, the total variation or total sum of squares is subdivided into explained variation or regression sum of squares (SSR), that which is due to the relationship between X and Y, and unexplained variation or error sum of squares (SSE), that which is due to factors other than the relationship ...

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