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

American Airlines

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.

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.