# 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

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.

 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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