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It Is Desired to Build a Regression Model to Predict y=\mathrm { y } =

Question 1

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It is desired to build a regression model to predict y=\mathrm { y } = the sales price of a single family home, based on the x1=x _ { 1 } = size of the house and x2=x _ { 2 } = the neighborhood the home is located in. The goal is to compare the prices of homes that are located in two different neighborhoods. The following complete 2nd-order model is proposed: E(y) =β0+β1x1+β2x12+β3x2+β4x1x2+β5x12x2\mathrm { E } ( \mathrm { y } ) = \beta _ { 0 } + \beta _ { 1 } \mathrm { x } _ { 1 } + \beta _ { 2 } \mathrm { x } _ { 1 } ^ { 2 } + \beta _ { 3 } \mathrm { x } _ { 2 } + \beta _ { 4 } \mathrm { x } _ { 1 } \mathrm { x } _ { 2 } + \beta _ { 5 } \mathrm { x } _ { 1 } ^ { 2 } \mathrm { x } _ { 2 } .
What hypothesis should be tested to determine if the quadratic terms are necessary to predict the sales price of a home?


A) H0:β2=β5=0\mathrm { H } _ { 0 } : \beta _ { 2 } = \beta _ { 5 } = 0
B) H0:β1=β2=β3=0\mathrm { H } _ { 0 } : \beta _ { 1 } = \beta _ { 2 } = \beta _ { 3 } = 0
C) H0:β1=β3=β4=0\mathrm { H } _ { 0 } : \beta _ { 1 } = \beta _ { 3 } = \beta _ { 4 } = 0
D) H0:β1=β2=β3=β4=β5=0\mathrm { H } _ { 0 } : \beta _ { 1 } = \beta _ { 2 } = \beta _ { 3 } = \beta _ { 4 } = \beta _ { 5 } = 0

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