SHOW ALL WORK EXCEL SHEETS AND USE DATA TABLE FROM PICTURE ABOVE ONLY FOR QUESTIONS THAT REQUIRE IT. 1. Develop the estimated regression equation relating speeding tickets to both the population and the number of people using Waze in these cities. Write it in the form TICKETS = ___ + ___ WAZE + ___ POP, where you fill in the missing values. Round to two decimals.2 .Is the population in the county a significant factor in determining the number of tickets? Why or why not? Use a=0.05 to test the level of significance.3. Predict the number of speeding tickets for a county with a population of 12,000 and 8,000 people using Waze.4. Does the estimated regression equation provide a good fit to the data? Explain.5. Now develop the estimated regression equation relating the number of speeding tickets to just the number of people using Waze. Write it in the form TICKETR = ____ + ____ WAZE, where you fill in the missing values. Round to two decimals.6. What is the adjusted R2 from the previous question?7. Which estimated equation do you prefer to explain speeding tickets (Q10 or Q14)? Why? (Hint: use previous question answers to explain).8. Would you drop either of the two variables (pop or Waze) included in the original regression equation (Q10)? Explain. (Hint: using previous answers will be helpful here.).9. Now suppose you did all of the work above and found the following for the estimated equation: TICKETS = 1244 + 102WAZE - 76POP. Interpret b1 and b2 in this new estimated regression equation. 10. Based on your interpretation from the previous question, do the results make sense? Why or why not?11. Suppose you wanted to see if political affiliation helps predict the number of speeding tickets at the city level. How exactly would you include this information in the regression analysis (you can assume people fall into only two categories - democrat or other)? (Note: please describe what the data would look like since this is qualitative information.)

For the following questions, we will look at the relationship between speeding tickets and two city-level variables: the number of people who use Waze (an app that, allegedly, tells you when there is a police officer somewhere ahead of you so you can slow down and not get a ticket) and population. Here is some data to use: # of people using Waze Population Tickets (1000s) (1000s) 16 896 19 1312 17 463 8.5 19 558 18 1556 9 18 1351 13 10 1336 9 16 1519 11 13 587 K 8 21 1484 N 18 1401 6.5 7 717 11 23 1421 5 7 939 22 1089