Explanation Multiple regression model Software procedure: Open the â€œ PackagingFoam 4â€� workbook. Select Stat > Regression > Regression > Fit regression model . Double click on column C3 to add â€œ Foam diameter â€� column to Reponses box. Double click on column C1 and C2 to add â€œ Die temperature â€� and â€œ Die diameter â€� to Continuous predictors box. Click Graphs . In the regression: Graphs dialog box, select the regular from residuals for plots pull-down list. Click on Graph s and check the Normal probability plot . Under the Residuals versus the variables, select all the three variables . Click OK in each dialogue box. The Minitab output shows that the multiple regression equation is, Foam density = 18.3 +0 .598×Die temperature-13 .51×Die diameter . The obtained residual plots are: The residual analysis shows that there is a possible violation of linearity and homogeneity assumptions. Testing the significance of overall model Step 1: The null and alternative hypotheses are given below: H 0 : There is no significant relationship between the foam diameter and two independent variables . H 1 : There is a significant relationship between the foam diameter and two independent variables . Step 2: The level of significance is 5 % . Step 3: The Minitab output obtained above shows that the test statistic for regression is F S T A T = 1.12 and p − value is 0.346. Step 4: Decision based on the p − value : If p − value < 0.05 , reject H 0 . Otherwise do not reject H 0 . Since the p − value is greater than 0.05, do not reject H 0 . Therefore, there is not enough evidence to conclude that there is a significant relationship between the foam diameter and two independent variables (die temperature and die diameter)

14th Edition

ISBN: 9780134684840

Author: BERENSON, Mark L., Levine, David M., Szabat, Kathryn A.

Publisher: Pearson,

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The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). Which regression equation is best for predicting city fuel consumption? Why? E Click the icon to view the table of regression equations. Choose the correct answer below. O A. The equation CITY = 6.65 - 0.00161WT + 0.675HWY is best because it has a low P-value and the highest adjusted value of R2. O B. The equation CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY is best because it has a low P-value and the highest value of R?. OC. The equation CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY is best because it uses all of the available predictor variables. O D. The equation CITY = - 3.14 + 0.823HWY is best because it has a low P-value and its R2 and adjusted R? values are comparable to…

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