528 CHAPTER 10 Correlation and Regression 26. Cheese and Engineering Listed below are annual data for various years. The data are weights (pounds) of per capita consumption of mozzarella cheese and the numbers of civil engineering PhD degrees awarded (based on data from the U.S. Department of Agriculture and the National Science Foundation). Is there sufficient evidence to conclude that there is a linear correlation between the two variables? Do the results suggest that consumption of mozzarella cheese causes people to earn PhD degrees in civil engineering? Cheese Consumption 9.3 9.7 9.7 9.7 9.9 10.2 10.5 11.0 10.6 10.6 Civil Engineering PhDs 480 501 540 552 547 622 655 701 712 708 27. Lemons and Car Crashes Listed below are annual data for various years. The data are weights (metric tons) of lemons imported from Mexico and U.S. car crash fatality rates per 100,000 population [based on data from “The Trouble with QSAR (or How I Learned to Stop Worrying and Embrace Fallacy),” by Stephen Johnson, Journal of Chemical Information and Modeling, Vol. 48, No. 1]. Is there sufficient evidence to conclude that there is a linear correlation between weights of lemon imports from Mexico and U.S. car fatality rates? Do the results suggest that imported lemons cause car fatalities? Lemon Imports 230 265 358 480 530 Crash Fatality Rate 15.9 15.7 15.4 15.3 14.9 28.Weighing Seals with a Camera Listed below are the overhead widths (cm) of seals measured from photographs and the weights (kg) of the seals (based on “Mass Estimation of Weddell Seals Using Techniques of Photogrammetry,” by R. Garrott of Montana State University). The purpose of the study was to determine if weights of seals could be determined from overhead photographs. Is there sufficient evidence to conclude that there is a linear correlation between overhead widths of seals from photographs and the weights of the seals? Overhead Width 7.2 7.4 9.8 9.4 8.8 8.4 Weight 116 154 245 202 200 191 Appendix B Data Sets. In Exercises 29–32, use the data from Appendix B to construct a scatterplot, find the value of the linear correlation coefficient r, and find either the P-value or the critical values of r from Table A-6 using a significance level of A = 0.05. Determine whether there is sufficient evidence to support the claim of a linear correlation between the two variables. 29. Taxis Repeat Exercise 15 using all of the time>tip data from the 703 taxi rides listed in Data Set 32 “Taxis” from Appendix B. Compare the results to those found in Exercise 15. 30. Taxis Repeat Exercise 16 using all of the distance>tip data from the 703 taxi rides listed in Data Set 32 “Taxis” from Appendix B. Compare the results to those found in Exercise 16. 31. Taxis Repeat Exercise 17 using all of the distance>fare data from the 703 taxi rides listed in Data Set 32 “Taxis” from Appendix B. Compare the results to those found in Exercise 17. 32. Taxis Repeat Exercise 18 using all of the time>fare data from the 703 taxi rides listed in Data Set 32 “Taxis” from Appendix B. Compare the results to those found in Exercise 18. Randomization. For Exercises 33–36, repeat the indicated exercise using the resampling method of randomization. 33.Powerball Jackpots and Tickets Sold Exercise 13 34.Powerball Jackpots and Tickets Sold Exercise 14 35. Taxis Exercise 15 36. Taxis Exercise 16 10-1 Beyond the Basics
RkJQdWJsaXNoZXIy NjM5ODQ=