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

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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. 

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