Use your knowledge of statistical methods to solve the following problems. Your graphing calculator will probably be a big help to you on most of these.

Judge Jeremy Jones (known as the Law of Speed Trap, South Carolina) is known far and wide for the strictness of his speeding fines. The speed limit within the town of Speed Trap is 55 mph. Interstate 55 (get it?) passes through his jurisdiction and has been generating a reliable source of revenue for Judge Jeremy’s county for many years. Recently a researcher made a study of the speed of people ticketed on the Interstate highway and the fine that Judge Jeremy assessed in each case. A representative subset of the data (shown below) might make a tempting regression study.

Fine assessed

Speed (according to radar)

$200

72 mph

$215

73 mph

$145

65 mph

$175

69 mph

$175

70 mph

$150

65 mph

$135

62 mph

$210

68 mph

13. If we are writing an article for a motorists’ club, we might want to be able to predict the likely fine that would result from various levels of speeding in Judge Jeremy’s area. What would be the explanatory variable? ______________________ What would be the response variable? _____________________

 

14. Code the speeds as speeds in excess of the speed limit, and create a histogram with divisions ("bins") at every multiple of 5 mph. Sketch your histogram here. What conclusions, if any, can you draw about the distribution of speeds from this histogram? Provide at least one sentence of explanation.

 

 

 

 

15. Compute the five-number summary for the coded speed data. No need to show work.

 

 

16. Compute the standard deviation of the coded speed data. No need to show work.

 

 

17. What is the standard deviation of the original speed data (i.e., the values ranging from 62 to 73)? Important: For this problem, you must either show your work (ugh!) or provide a sentence of explanation.

18. Create a SCATTERPLOT and a suitable regression line that we might be able to use as a way of predicting Judge Jeremy’s fines. Draw your scatterplot and regression line here.

 

 

 

 

 

 

 

 

19. What is the slope of your regression line from #18? ___________________________

20. Is Judge Jeremy fairly predictable in his assessment of speeding fines? What can you say about the correlation between speed and fine? (Write your answer using AP-style wording.)

 

 

 

 

21. In our article for the motor club, what would be a reasonable estimate of the fine Judge Jeremy would slap on a speeder who was clocked doing 70 mph? Show your work.

 

 

 

 

22. What would Judge Jeremy fine a speeder who was clocked at 57 mph? Is it reasonable to make such a prediction? Why or why not?

 

 

 

 

23. The speeder in the sample data who was going 68 mph was given a surprisingly hefty fine (surprising, that is, in terms of the regression line). What term do we apply to this case? ________________________

24. Remove the speeder who was going 68 mph from your data set and recompute the regression line. What is the new slope? _____________________

25. Is the case of the speeder who was going 68 mph an influential observation? ___________ Why or why not? _______________________________________

26. Sketch the residual plot here for the original data set. (In other words, make sure the case of the 68-mph speeder is included.) Does your residual plot suggest any systematic problem with the regression line? _____________ Explain your answer. ____________________________________________________