In Modules 1 and 4 you used some data you collected on two airlines, along with some data on the airline industry. Use the same data to perform a regression using load factor as the independent variable and revenue passenger miles as the dependent variable for one of your airlines. Summarize your results and include a description of what you would anticipate the relation between the two variables to be and what the actual results indicate. Are the results statistically significant? Be sure to include a table summarizing your results and a scatterplot of your data that includes the resulting model. Make sure and examine the plots discussed in this module regarding normality
Allen Chiu
Dr. Arnold Witchel
MBAA 522 Business Research Methods
4.3 – Data Assignment
JetBlue and AirTrans
For both airlines, (recall you already collected data on one airline in Module 1 and an additional airline as part of this assignment), construct 95 percent confidence intervals (alpha would equal what in this case?) for monthly load factors, monthly revenue passenger miles, and monthly available seat miles (Domestic flights only). There is a function in Excel that will calculate the confidence interval that needs to be added and subtracted from the mean to determine the 95 percent confidence interval.
Alaska Airlines
95% CI for AA's Monthly Load Factors
Column1
Mean
80.37011905
Standard Error
0.600998655
Median
81.12
Mode
77.14
Standard Deviation
5.508243656
Sample Variance
30.34074818
Kurtosis
-0.219644259
Skewness
-0.566204217
Range
23.46
Minimum
65.38
Maximum
88.84
Sum
6751.09
Count
84
Confidence Level(95.0%)
1.195362152
95% CI for AA's Monthly Revenue Passenger Miles
Column1
Mean
1476562.262
Standard Error
29349.50149
Median
1471640.5
Mode
#N/A
Standard Deviation
268992.6244
Sample Variance
72357031976
Kurtosis
-0.527253811
Skewness
0.340340669
Range
1145447
Minimum
993212
Maximum
2138659
Sum
124031230
Count
84
Confidence Level(95.0%)
58374.97804
95% CI for AA's Monthly Available Seat Miles
Column1
Mean
58273652.57
Standard Error
431032.5637
Median
58427623
Mode
#N/A
Standard Deviation
3950478.7
Sample Variance
1.56063E+13
Kurtosis
-0.510882376
Skewness
-0.217090741
Range
17844002
Minimum
48005940
Maximum
65849942
Sum
4894986816
Count
84
Confidence Level(95.0%)
857306.433
American Airlines
95% CI for AMA's Monthly Load Factors
Column1
Mean
82.93404762
Standard Error
0.433180016
Median
83.355
Mode
84.56
Standard Deviation
3.970160423
Sample Variance
15.76217378
Kurtosis
-0.817952783
Skewness
-0.15936971
Range
15.03
Minimum
74.91
Maximum
89.94
Sum
6966.46
Count
84
Confidence Level(95.0%)
0.861577629
95% CI for AMA's Revenue Passenger Miles
Column1
Mean
6624897.464
Standard Error
78575.74199
Median
6522230
Mode
#N/A
Standard Deviation
720158.5709
Sample Variance
5.18628E+11
Kurtosis
-0.55372059
Skewness
0.291945709
Range
3068996
Minimum
5208159
Maximum
8277155
Sum
556491387
Count
84
Confidence Level(95.0%)
156283.9905
95% CI for AMA's Monthly Available Seat Miles
Column1
Mean
7984735.06
Standard Error
81228.32381
Median
7753371.5
Mode
#N/A
Standard Deviation
744469.8849
Sample Variance
5.54235E+11
Kurtosis
-1.033899839
Skewness
0.403902886
Range
2689869
Minimum
6734620
Maximum
9424489
Sum
670717745
Count
84
Confidence Level(95.0%)
161559.8691
Develop the appropriate null and alternate hypotheses and test if the monthly load factors, monthly revenue passenger miles, and monthly available seat miles are equal for the two airlines (use alpha of 0.05). In addition, using the results from module 1 where you calculated the summary statistics for the items listed, test if the mean for each airline is equal to the mean for the industry for monthly load factors, monthly revenue passenger miles, and monthly available seat miles.
Null (H0) = The monthly load factors, monthly revenue passenger miles, and monthly available
seat miles are not equal for the two airlines.
Alternative (H1) = The monthly load factors,
monthly revenue passenger miles, and monthly available seat miles are equal for the two airlines.
95% Level of Significance (alpha 0.05)
All US Carriers
Alaska Airlines
American Airlines
Differences between US Carriers &Alaska
Differences between US Carriers &American
Montly Load Factors
81.15
80.37
82.93
0.78
-1.78
Monthly Revenue Passenger Miles
47184253.73
1476562.262
6624897.464
0.035%
0.14%
Monthly Available Seat Miles
580647893.89
58273652.57
7984735.06
0.10%
0.01%
Airline data shows mean does not equal for industry monthly load factors, monthly revenue passenger miles, and monthly available seat miles. This would suggest that we accept the null hypothesis HO because data are not equal for Alaska and American. Also we are rejecting the alternative hypothesis H1 because it assumes monthly airline data are equal for both airlines.
Monthly load factors for Alaska is lower compared to US data because Alaska operates within a small section of US air travel market – they are a relative small company compared to other airlines. American airlines has higher monthly load factors because they are a larger established airline that operates in all areas of the US.
All US carriers’ mean monthly revenue passenger miles is 47184253.73,Alaska 58273652.57, and American 7984735.06. Alaska’s revenue represents 0.035% while American revenue is 0.14%, of all US carrier revenue. Alaska’s available seat miles are 0.10% while American is 0.01% which suggests Alaska has more availability on seat miles vs American. This also suggests that American’s seat capacity is close to full on their flights.