1. Given the following data where city MPG is the response variable and weight is the explanatory variable, explain why a regression line would be appropriate to analyze the relationship between these variables:
The linear regression line will show the relationship between the MPG and weight. It will also show the value of dependency weight has on the MPG. MPG is dependent on the weight in this particular study.
Model City MPG Weight
Mazda MX-5 Miata 25 2365
Mercedes/Benz SLK 22 3020
Mitsubishi Eclipse 23 3235
Pontiac Firebird 18 3545
Porsche Boxster 19 2905
Saturn SC 27 2420
2. Construct the regression line for this data.
3. Interpret the meaning of the y-intercept and the slope within this scenario.
4. What would you predict the city MPG to be for a car that weighs 3000 pounds?
5. If a car that weighs 3000 pounds actually gets 32 MPG, would this be unusual? Calculate the residual and talk about what that value represents.
Review the data in the following table.
College Graduate Not a College Graduate Total
Male 56 32 88
Female 62 41 103
Total 118 73 191
6. Have the assumptions for this test been met?
7. Why or why not?
8. State the null and alternative hypothesis for this test.
9. Calculate the test statistic for this test. Explain what this test statistic represents.
10. Use technology, like Excel, to calculate the p-value for this test. Explain what this p-value represents.
11. State the conclusion for this test at the 0.05 level of significance.
12. Do you think these variables are dependent/associated? Why or why not?
The solution gives detailed steps on solving two short answer questions: one on simple regression analysis and one on contigency table.