Answer false type tf difficulty difficult keywords

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ANSWER: False TYPE: TF DIFFICULTY: Difficult KEYWORDS: collinearity, properties 26. True or False: Collinearity is present if the dependent variable is linearly related to one of the explanatory variables. ANSWER: False TYPE: TF DIFFICULTY: Easy KEYWORDS: collinearity, properties 27. True or False: Collinearity will result in excessively low standard errors of the parameter estimates reported in the regression output. ANSWER: False TYPE: TF DIFFICULTY: Difficult KEYWORDS: collinearity, properties 28. True or False: The parameter estimates are biased when collinearity is present in a multiple regression equation. ANSWER: False TYPE: TF DIFFICULTY: Difficult KEYWORDS: collinearity, properties
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Multiple Regression Model Building 207 29. True or False: Two simple regression models were used to predict a single dependent variable. Both models were highly significant, but when the two independent variables were placed in the same multiple regression model for the dependent variable, R 2 did not increase substantially and the parameter estimates for the model were not significantly different from 0. This is probably an example of collinearity. ANSWER: True TYPE: TF DIFFICULTY: Moderate KEYWORDS: collinearity, properties 30. True or False: So that we can fit curves as well as lines by regression, we often use mathematical manipulations for converting one variable into a different form. These manipulations are called dummy variables. ANSWER: False TYPE: TF DIFFICULTY: Moderate KEYWORDS: quadratic regression, transformation TABLE 15-4 A chemist employed by a pharmaceutical firm has developed a muscle relaxant. She took a sample of 14 people suffering from extreme muscle constriction. She gave each a vial containing a dose ( X ) of the drug and recorded the time to relief ( Y ) measured in seconds for each. She fit a “centered” curvilinear model to this data. The results obtained by Microsoft Excel follow, where the dose ( X ) given has been “centered.” SUMMARY OUTPUT Regression Statistics Multiple R 0.747 R Square 0.558 Adjusted R Square 0.478 Standard Error 863.1 Observations 14 ANOVA df SS MS F Signif F Regression 2 10344797 5172399 6.94 0.0110 Residual 11 8193929 744903 Total 13 18538726 Coeff StdError t Stat p-value Intercept 1283.0 352.0 3.65 0.0040 CenDose 25.228 8.631 2.92 0.0140 CenDoseSq 0.8604 0.3722 2.31 0.0410
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208 Multiple Regression Model Building 31. Referring to Table 15-4, the prediction of time to relief for a person receiving a dose of the drug 10 units above the average dose (i.e., the prediction of Y for X = 10), is ________. ANSWER: 1,621 TYPE: FI DIFFICULTY: Moderate KEYWORDS: quadratic regression, prediction of individual values 32. Referring to Table 15-4, suppose the chemist decides to use an F test to determine if there is a significant curvilinear relationship between time and dose. The p -value of the test is ________. ANSWER: 0.041 TYPE: FI DIFFICULTY: Difficult KEYWORDS: quadratic regression, partial F test, p -value 33. Referring to Table 15-4, suppose the chemist decides to use an F test to determine if there is a significant curvilinear relationship between time and dose. The value of the test statistic is ________.
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Christopher Reinemann
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