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Unformatted text preview: Homework 2 Solution to Additional Problems 1. FOOD 30 20 10 PRICE 60 50 40 30 20 10 DECOR 30 20 10 PRICE 60 50 40 30 20 10 SERVICE 28 26 24 22 20 18 16 14 12 10 PRICE 60 50 40 30 20 10 All the three predictors have approximately positive linear relationship with price. 2. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .556(a) .310 .304 8.2282 a Predictors: (Constant), FOOD Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .722(a) .522 .518 6.8466 a Predictors: (Constant), DECOR Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .697(a) .486 .482 7.1002 a Predictors: (Constant), SERVICE DÉCOR seems the best linear predictor of the price. 3. Coefficients(a) Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant )12.953 4.6772.769 .006 FOOD 1.649 .221 .556 7.457 .000 a Dependent Variable: PRICE The estimate of the slope of the regression line is 1.649, which tells us that with one unit increase in food quality, the price will be increased by 1.649. 4. Ho: beta=0 Ha: beta not equal to 0. Test statistics: t = 7.457, with pvalue< 0.001. So, we conclude that there is linear dependence of price on quality of food. 5. ANOVA(b) Model Sum of Squares df Mean Square F Sig. 1 Regressio n 7230.194 3 2410.065 59.649 .000(a) Residual 4929.298 122 40.404 Total 12159.492 125 a Predictors: (Constant), SERVICE, DECOR, FOOD b Dependent Variable: PRICE Coefficients(a) Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant )18.505 3.6645.050 .000 FOOD.026 .293.009.089 .930 DECOR 1.058 .185 .460 5.712 .000 SERVICE 1.137 .342 .383 3.327 .001 a Dependent Variable: PRICE 6. y = 18.5050.026*X1+1.058*X2+1.137*X3 Where X1, X2 and X3 stand for food, decor or service, respectively....
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This note was uploaded on 07/08/2011 for the course STA 6127 taught by Professor Mukherjee during the Fall '08 term at University of Florida.
 Fall '08
 Mukherjee

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