In modules 1 and 4 you used some data you collected on two airlines,

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.

In modules 1 and 4 you used some data you collected on two airlines,

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