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A Visual Approach to SPSS for Windows 2nd Edition by Leonard Stern
Edition 2ISBN: 978-0205706051A Visual Approach to SPSS for Windows 2nd Edition by Leonard Stern
Edition 2ISBN: 978-0205706051 Exercise 2
A person investigating the relation between car weight and gas mileage reasons that because heavier cars tend to have bigger engines and because bigger engines weigh more, any relation between car weight and gas mileage may be "contaminated" by engine size. Use the data in the file 04Cars.sav (see previous problem) to assess the relation between gas mileage (use HiwayMPG ) and car weight ( Weight ) controlling for (i.e., over and above) the influence of engine size ( Engine ) on gas mileage. As in exercise 1, omit the data of the hybrid vehicles (case numbers 69, 70, and 94) before performing the analysis.
Exercise 1
For this exercise, use the file 04Cars.sav available on the publisher's website for this text at the address http://www.pearsonhighered.com/stern2e. The data set is based on information obtained from Edmunds.com and is reprinted here with permission. Perform a multiple linear regression analysis to predict a car's highway gas mileage (variable HiwayMPG ) from the following variables: Engine , Cylinders , HP , Weight , Wheelbase , and Length. Before performing the analysis, omit the data of the hybrid vehicles (case numbers 69, 70, and 94). Enter all the predictor variables simultaneously. From your printout, answer the following questions.
1. Does it appear that the distributional assumptions of the residuals have been met
2. What is the numerical value of the simple correlation between the dependent variable and each predictor variable
3. Considering just the predictor variables, which two are most strongly related
4. What proportion of variance of the variable HiwayMPG is explained by all the predictors combined Is that proportion statistically significant
5. What is the equation that predicts HiwayMPG from the (unstandardized) predictors
6. Using semipartial correlation values, list, in order of magnitude, the three variables in the equation most effective in predicting the value of HiwayMPG.
7. Are there any cars in the data set that produce residuals that could be considered outliers If so, which car(s)
Exercise 1
For this exercise, use the file 04Cars.sav available on the publisher's website for this text at the address http://www.pearsonhighered.com/stern2e. The data set is based on information obtained from Edmunds.com and is reprinted here with permission. Perform a multiple linear regression analysis to predict a car's highway gas mileage (variable HiwayMPG ) from the following variables: Engine , Cylinders , HP , Weight , Wheelbase , and Length. Before performing the analysis, omit the data of the hybrid vehicles (case numbers 69, 70, and 94). Enter all the predictor variables simultaneously. From your printout, answer the following questions.
1. Does it appear that the distributional assumptions of the residuals have been met
2. What is the numerical value of the simple correlation between the dependent variable and each predictor variable
3. Considering just the predictor variables, which two are most strongly related
4. What proportion of variance of the variable HiwayMPG is explained by all the predictors combined Is that proportion statistically significant
5. What is the equation that predicts HiwayMPG from the (unstandardized) predictors
6. Using semipartial correlation values, list, in order of magnitude, the three variables in the equation most effective in predicting the value of HiwayMPG.
7. Are there any cars in the data set that produce residuals that could be considered outliers If so, which car(s)
Explanation
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Using the data in the file 04Cars.sav to...
A Visual Approach to SPSS for Windows 2nd Edition by Leonard Stern
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