A realtor used the regression model, y = beta 0 + beta 1 x 1 +beta 2 x 2 + epsil

Question

A realtor used the regression model, y = beta0 + beta1x1 +beta2x2 + epsilon, to predict selling prices of homes (in thousands of $) in a Columbus suburb. The variable x1 represents the home size (square feet), and x2 represents the number of bedrooms. The following Excel partial output is available.

If for a fixed square footage, a house has one extra bedroom, the predicted selling price        

ANOVA df SS MS F Regression 2 5800.44 2900.22 92.66 Residual 10 312.99 31.30 Total 12 6113.43 Coefficients Standard Error t Stat Intercept 27.13 22.80 1.19 Size 0.16 0.03 5.33 Bedrooms 20.22 6.42 3.15

Explanation / Answer

Y = Intercept+ Coeffecient of Size* Actual size + Coefficient of bedroom * no. of bedrooms,

Y = 27.13+ 0.16*x+20.22*z

THe predicted selling price for one extra bedroom and fixed size would be greater than the coefficient of no. of bedrooms

Hence, predicted selling price would increase by 20.22

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