With , the Sum of Squared Residuals (Error Sum of Squares)

Short Answer

With $\hat { y } _ { i } = \hat { \beta } _ { 0 } + \hat { \beta } _ { 1 } x _ { i } + \hat { \beta } _ { 2 } x _ { i } ^ { 2 } + \cdots \cdots + \hat { \beta } _ { k } x _ { i } ^ { k }$ , the sum of squared residuals (error sum of squares) is $\operatorname { SSE } = \sum \left( y _ { i } - \hat { y } _ { i } \right) ^ { 2 }$ . Hence the mean square error is MSE =__________/___________.