A regression and correlation analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x). $$\begin{array} { l l }
\Sigma x = 90 & \sum \left( y - \frac { y } { x } \right) \left( x - { } ^ { x } \right) = 466 \
\Sigma y = 170 & \sum ( x - ) ^ { 2 } = 234 \
n = 10 & \Sigma \left( y - y ^ { 2 } \right) ^ { 2 } = 1434 \
\mathrm { SSE } = 505.98 &
\end{array}$$ The sum of squares due to regression (SSR) is
A) 1434.
B) 505.98.
C) 50.598.
D) 928.02.

Question

A regression and correlation analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x). $$\begin{array} { l l } 
\Sigma x = 90 & \sum \left( y - \frac { y } { x } \right) \left( x - { } ^ { x } \right) = 466 \
\Sigma y = 170 & \sum ( x - ) ^ { 2 } = 234 \
n = 10 & \Sigma \left( y - y ^ { 2 } \right) ^ { 2 } = 1434 \
\mathrm { SSE } = 505.98 &
\end{array}$$  The sum of squares due to regression (SSR) is
A) 1434.
B) 505.98.
C) 50.598.
D) 928.02.

Quizplus · Accepted Answer

SSR is calculated as SST - SSE. SST (Total Sum of Squares) is given by the sum of squares of y minus the mean of y, which is 1434. SSE (Sum of Squares due to Error) is given as 505.98. Therefore, SSR = 1434 - 505.98 = 928.02.

A Regression and Correlation Analysis Resulted in the Following Information