# Case 28: Housing Prices

2112 Words9 Pages
Case 28: Housing Prices GM533 Managerial Statistics April 11, 2012 I’m conducting an analysis between the price of a home in Eastville, Oregon and the factors which develop a home’s price. The data is analyzed using ANOVA (Analysis of Variance) and multiple regression hypothesis testing procedures. The regression analysis will help create a multiple regression fit which will incorporate the ten predictor variables of a home’s price. After the regression analysis is complete, global and local ANOVA tests will help eliminate the insignificant predictor variables and create the net significant regression equation. Even though the sample size is only representative of the houses in Oregon, the general trends that affect house prices are…show more content…
Calculate test statistic 4. Compare with the decision rule 5. Determine the final decision The hypotheses for this test are: Null hypothesis:b1-10=0 Alternative hypothesis:b1 and or 2 and or 3 and or 4…and or 10≠0 The null hypothesis consists of all independent variables that have a regression coefficient which is equal to zero; the alternative hypothesis is at least one independent variable that has a regression coefficient which is not zero. The rejection of the hypotheses will be done via the traditional method. In this method, the test statistic is calculated and then the p-value is computed as well. The p-value is compared with the significance level of 0.05, and then the decision is made whether to reject the null hypothesis or not. The test statistic calculated from Minitab is about 45.91 shown in the following information: The regression equation is PRICE = - 15.2 + 0.0376 SQFT + 4.92 BEDS - 2.91 BATHS - 12.9 HEAT + 2.29 STYLE + 15.8 GARAGE + 9.08 BASEMENT - 1.03 AGE + 5.31 FIRE + 4.62 SCHOOL Predictor Coef SE Coef T P Constant -15.212 9.818 -1.55 0.125 SQFT 0.037596 0.003627 10.36 0.000 BEDS 4.924 1.965 2.51 0.014 BATHS -2.912 3.024 -0.96 0.338 HEAT -12.910 6.101 -2.12 0.037 STYLE 2.288 1.644 1.39 0.167 GARAGE 15.759 3.825 4.12 0.000 BASEMENT 9.077 3.445 2.63 0.010 AGE -1.0342 0.2813 -3.68