10 points Case Study 2 (Alumni Giving). Refer to file named Alumni-Giving.xlxs for data. Alumni donations are an important source of revenue for colleges and universities. If administrators could determine the factors that could lead to increases in the percentage of alumni who make a donation, they might be able to implement policies that could lead to increased revenues. Research shows that students who are more satisfied with their contact with teachers are more likely to graduate. As a result, one might suspect that smaller class sizes and lower student-faculty ratios might lead to higher percentage of satisfied graduates, which in turn might lead to increases in the percentage of alumni who make a donation. The file Alumni-Giving.xlxs shows the data for 48 top universities in USA. The column labeled Graduation cap rate is the percentage of students who initially enrolled at the University and graduated. The column labeled percentage of Classes Under 20 shows the percentage of classes offered with fewer than 20 students. The column labeled Student-Faculty The ratio is a number of students enrolled divided by the total number of faculty. Finally, the column labeled Alumni Giving Rate is the percentage of alumni who make a donation to the University. Based on this data develop an estimated regression equation that could be used to reliably predict the alumni giving rate with as few input variables as possible. State your results briefly. 25 points Case Study 3 (North-South Airline). Refer to file named North-South-Airline.xlxs for data Recently, Northern Airlines merged with Southeast Airlines to create the fourth largest US carrier. The new North-South Airline inherited both an aging fleet of Boeing 727-300 aircraft and Stephen Drew. Stephen was a tough former Secretary of the Navy who stepped in as new president and chairman of the board. Stephen’s first concern in creating a financially solid company was maintenance costs. It was commonly surmised in the airline industry that maintenance costs rise with the age of the aircraft. He quickly noticed that historically there had been a significant difference in the reported B727-300 maintenance costs (data obtained from ATA Form 40 1s) in both the airframe and engine areas between Northern Airlines and Southeast Airlines, with Southeast having the newer fleet. Shortly after the merger, Peg Jones, vice president for operations and maintenance, was called into Stephen’s office and asked to study the issue. Specifically, Stephen wanted to know whether the average fleet age was correlated to direct airframe maintenance costs, and whether there was a relationship between average fleet age and direct engine maintenance costs. Peg was to report back with an answer, along with quantitative and graphical descriptions of the relationship. Peg’s first step was to have her staff construct the average age of Northern and Southeast B727-300 fleets, by quarter, since the introduction of that aircraft to service by each airline in the early 90s. The average age of each fleet was calculated by first multiplying the total number of calendar days each aircraft had been in service at the pertinent point in time by the average daily utilization of the respective fleet to total fleet hours flown. The total fleet hours flown was then divided by the number of aircraft in service at that time, giving the age of the “average” aircraft in the fleet. The average utilization was found by taking the actual total fleet hours flown on one specific day before the merger, from Northern and Southeast data, and dividing by the total days in service for all aircraft at that time. The average utilization for Southeast was 8.3 hours per day, and the average utilization for Northern was 8.7 hours per day. Because the available cost data were calculated for each yearly. Ending at the end of the first quarter, average fleet age was calculated at the same points in time. The fleet data are shown in the file North-South-Airline.xlxs. Airframe cost data and engine cost data are both shown paired with fleet average age in that table. 10 points. (a) Develop two separate regression models to predict the Airframe Cost per aircraft and the Engine Cost per aircraft based on the Average Age of an aircraft for Northern Airlines. Comment on the strength of the relationship between Average Age of an aircraft owned by Northern Airlines and the Airframe Cost per aircraft as well as the Engine Cost per aircraft. Specifically, comment on whether or not the age of an aircraft appears to be the sole, or even primary, determinant of maintenance costs at Northern Airlines. 10 points. (b) Develop two separate regression models to predict the Airframe Cost per aircraft and the Engine Cost per aircraft based on the Average Age of an aircraft for Southeast Airlines. Comment on the strength of the relationship between Average Age of an aircraft owned by Southeast Airlines and the Airframe Cost per aircraft as well as the Engine Cost per aircraft. Specifically, comment on whether or not the age of an aircraft appears to be the sole, or even primary, determinant of maintenance costs at Southeast Airlines. 5 points. (c) Stephen has always believed that there is a difference in maintenance procedures between Northern Airlines and Southeast Airlines that has a tangible impact on aircraft maintenance costs. Do the data support this claim? Explain your answer.  The application is real; nonetheless, the numbers may have been scaled to prevent detection but do not affect the final results of the analysis.  Note: the application is real; nonetheless, the numbers may have been scaled to prevent detection but do not affect the final results of the analysis.  Note: the application is real; nonetheless, the numbers may have been scaled to prevent detection but do not affect the final results of the analysis.