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 least squares estimate of the intercept or b₀ equals A) .923. B) 1.991. C) -1.991. D) -.923.

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The Answer of 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 least squares estimate of the intercept or b₀ equals A) .923. B) 1.991. C) -1.991. D) -.923.

A Regression and Correlation Analysis Resulted in the Following Information