Question

The project is adapted from the Chapter 4 Case Study dealing with North–South Airline In January 2012, Northern Airlines merged with Southeast Airlines to create the fourth largest U.S. carrier. The new North–South Airline inherited both an aging fleet of Boeing 727-300 aircraft and Stephen Ruth. 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 (from ATA Form 41s) in both the airframe and the engine areas between Northern Airlines and Southeast Airlines, with Southeast having the newer fleet.

On February 12, 2012, 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 by February 26 with the answer, along with quantitative and graphical descriptions of the relationship.

Peg’s first step was to have her staff construct the average age of the Northern and Southeast B727-300 fleets, by quarter, since the introduction of that aircraft to service by each airline in late 1993 and early 1994. 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 determine the 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 September 30, 2011, 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 period 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 following table.

Airframe cost data and engine cost data are presented below (please note, I have altered the number presented in the text so that online solutions cannot be used) paired with fleet average age in that table.

The project requirements are:

1. Prepare Excel Data Analysis Regression Tables demonstrating your excellence at determining Northern and Southeast Costs to Average Age. Besides the data tables, copied from the project instructions, four regression models are required each on a separate tab. Place each regression model with supporting data labels, line fit plots, and other required items on a separate worksheet tab.
2. On each worksheet tab (other than the data table tab) include:
   1. a copy of your data entry screen (Use Alt+Print Screen to copy picture of Regression Data Entry from Data Analysis in Excel and paste on correct worksheet tab).
   2. The regression model derived from the data tables.
   3. Line Fit Plot for each Worksheet tab.
   4. Labels of the data included.
   5. Highlight with yellow and label the following four items on each regression model:
      1. Coefficient of determination
      2. Coefficient of correlation or covariance
      3. Slope, and
      4. Beta or intercept
3. Finally prepare a formal response, using Microsoft Word, from Peg Jones’s to Stephen Ruth explaining your numbers and calculations. Which costs are correlated with the average age of the aircraft? What is the slope and beta? Explain the coefficient of determination and covariance. Explain how this information benefits each airline.

Please provide a detailed explanation of each step. I'm not sure how to get 4 regression analysis'.

Submit your Excel Worksheet with five tabs (data, plus 4 tabs for the regressions) to the assignment drop box. Also include your formal response in a Microsoft Word document.

Note: Dates and names of airlines and individuals have been changed in this case to maintain confidentiality. The data and issues described here are real.

Northern Airline Data (numbers have been changed from text)

	Airframe Cost	Engine Cost	Average Age
Year	per Aircraft	per Aircraft	(Hours)
2001	61.80	33.49	6,512
2002	54.92	38.58	8,404
2003	69.70	51.48	11,077
2004	68.90	58.72	11,717
2005	63.72	45.47	13,275
2006	84.73	50.26	15,215
2007	78.74	80.60	18,390

Southeast Airline Data (numbers have been changed from text)

	Airframe Cost	Engine Cost	Average Age
Year	Per Aircraft	per Aircraft	(Hours)
2001	14.29	19.86	5,107
2002	25.15	31.55	8,145
2003	32.18	40.43	7,360
2004	31.78	22.10	5,773
2005	25.34	19.69	7,150
2006	32.78	32.58	9,364
2007	35.56	37.07	8,259

Thank you!

Answer 1

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.

Soln

Steps for Regression in Excel

•             Import the data in Excel

•             In Data Analysis tool from Data Tab.

•             Click on regression and select the Y values and X values. Select Labels and click OK.

Northern Airline

i)

Northern Airline

Average Age v/s Airframe Cost

Average Age v/s Airframe Cost 20,000 18,000 16,000 14,000 y = 315.19x - 9641.7 R2 = 0.6375 Average Age 10,000 8,000 6,000 4,000 50 55 60 65 75 80 85 90 70 Airframe Cost

Regression Output

Regression Statistics
Multiple R	0.7985
R Square	0.6375
Adjusted R Square	0.5650
Standard Error	2648.0103
Observations	7
ANOVA
	df	SS	MS	F	Significance F
Regression	1	616,65,806.50	616,65,806.50	8.79	0.03
Residual	5	350,59,792.93	70,11,958.59
Total	6	967,25,599.43
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	-9,641.66	7,394.21	-1.30	0.25	-28,649.08	9,365.75
Airframe Cost	315.19	106.28	2.97	0.03	41.98	588.40

Regression Equation

Average Age = 315.19 * Airframe Cost -9,641.66

Coefficient of determination = 0.6375

Correlation Coefficient = 0.7985

Slope = 315.19

Intercept = -9,641.66

Since the p-value of the ANOVA table is less than 0.05, the model is significant. There is moderate correlation between the two variables. Also the R Square tells that 63.75% of the variation in Average Age can be explained by Airframe Cost.

ii)

Northern Airline

Average Age v/s Engine Cost

Average Age v/s Engine Cost 20,000 18,000 y=222.55x+683.56 R2 = 0.731 Average Age 16,000 14,000 12,000 8,000 6,000 4,000 30 40 50 70 80 90 60 Engine Cost

Regression Output
Regression Statistics
Multiple R	0.8550
R Square	0.7310
Adjusted R Square	0.6772
Standard Error	2281.2607
Observations	7
ANOVA
	df	SS	MS	F	Significance F
Regression	1	707,04,848.57	707,04,848.57	13.59	0.01
Residual	5	260,20,750.86	52,04,150.17
Total	6	967,25,599.43
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	683.56	3,210.95	0.21	0.84	-7,570.45	8,937.58
Engine Cost	222.55	60.38	3.69	0.01	67.34	377.75

Regression Equation

Average Age = 683.56 + 222.55 * Engine Cost

Coefficient of determination = 0.7310

Correlation Coefficient = 0.8550

Slope = 222.55

Intercept = 683.56

Since the p-value of the ANOVA table is less than 0.05, the model is significant. There is strong correlation between the two variables. Also, the R Square tells that 73.10% of the variation in Average Age can be explained by Engine Cost.

Hence, for Northers Airlines, Average Age is impacted by both Airframe and Engine Cost