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The Model E(y)=β0+β1x1+β2x2+β3x3+β4x4E ( y ) = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 } + \beta _ { 3 } x _ { 3 } + \beta _ { 4 } x _ { 4 }

question 114

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The model E(y)=β0+β1x1+β2x2+β3x3+β4x4E ( y ) = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 } + \beta _ { 3 } x _ { 3 } + \beta _ { 4 } x _ { 4 } was used to relate E(y)E ( y ) to a single qualitative variable, where
x1={1 if level 2 0 if not x2={1 if level 30 if not x3={1 if level 4x40 if not {1 if level 50 if not \begin{array} { l l } x _ { 1 } = \left\{ \begin{array} { l l } 1 & \text { if level 2 } \\0 & \text { if not }\end{array} \right. & x _ { 2 } = \left\{ \begin{array} { l l } 1 & \text { if level } 3 \\0 & \text { if not }\end{array} \right. \\x _ { 3 } = \left\{ \begin{array} { l l l } 1 & \text { if level } 4 & x _ { 4 } \\0 & \text { if not }\end{array} \right. & \left\{ \begin{array} { l l } 1 & \text { if level } 5 \\0 & \text { if not }\end{array} \right.\end{array}
This model was fit to n=40n = 40 data points and the following result was obtained:
y^=14.5+3x14x2+10x3+8x4\hat { y } = 14.5 + 3 x _ { 1 } - 4 x _ { 2 } + 10 x _ { 3 } + 8 x _ { 4 }
a. Use the least squares prediction equation to find the estimate of E(y)E ( y ) for each level of the qualitative variable.
b. Specify the null and alternative hypothesis you would use to test whether E(y)E ( y ) is the same for all levels of the independent variable. 3 Test if Model is Useful for Predicting y


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