In regression analysis, the total variation in the dependent variable y, measured by $\sum \left( y _ { i } - \bar { y } \right) ^ { 2 }$ , can be decomposed into two parts: the explained variation, measured by SSR, and the unexplained variation, measured by SSE.

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In regression analysis, the total variation in the dependent variable y is indeed decomposed into the explained variation (SSR: Sum of Squares due to Regression) and the unexplained variation (SSE: Sum of Squares due to Error), reflecting the parts of the total variation that are explained by the model and the part that is not, respectively.

In Regression Analysis, the Total Variation in the Dependent Variable ∑(yi−yˉ)2\sum \left( y _ { i } - \bar { y } \right) ^ { 2 }∑(yi​−yˉ​)2

In Regression Analysis, the Total Variation in the Dependent Variable $\sum \left( y _ { i } - \bar { y } \right) ^ { 2 }$