Mba_510_Business_Problem

Running head: BUSINESS PROBLEM Business Problem Learning Team University of Phoenix Business Problem The Air Transport Association of America is the premier trade group of the principal airlines in the United States. ATA airline members and their affiliates transport more than 90% of U.S. airline passenger and cargo traffic (ATA, 2009). The Airlines Transportation Association of America (ATA) played a major role in the creation of the Civil Aeronautics Board and the Federal Aviation Administration. The ATA reported in 2009 that “In 2008, every dollar increase per barrel (42 gallons) drove an additional $464M in fuel expenses for U.S. passenger and cargo airlines; every penny increase per gallon drove $195M in annual expenses.” The rising cost of fuel affects the operating costs and profits of commercial air carriers forcing airlines to find creative ways of saving and recouping costs. “Domestic aircraft size increased in 2008 by 0.4 seats to 120.8 seats. The increase was partly caused by an unprecedented jump in aircraft size by the regional carriers (up 2.9 seats) and the grounding of older, fuel inefficient aircraft (i.e. MD-80’s and 737-300/400/500) by the mainline carriers. The increase in regional aircraft size was caused by the retirement of 50-seat jet aircraft as larger 70-90 seat jet aircraft entered the fleet” (Federal Aviation Administration, 2009). Other cost cutting measures in addition to increases in aircraft size and retiring less fuel-efficient aircraft were consolidation of flight schedules, use of lightweight, low-density aircraft parts, and assessment of baggage fees, frequent flyer incentives, and the reduction of free passenger amenities. Commercial airlines have struggled for years with the rising cost of fuel. Historically, According to the ATA, “… fuel expenses have ranged from 10 percent of U.S. passenger airline operating costs, but averaged more than 35 percent in the third quarter of 2008” (Federal Aviation Commission, 2009). Several commercial airlines looked for alternate methods of saving money during the fuel related economic crisis of 2008. This paper will review the total revenue for six major airlines: United, American, Northwest, Delta, Continental, and US Airlines during the height of the fuel cost crisis in 2008. It compares the cost of fuel and the amount of fuel used for each airline to determine if the independent attribute, either fuel oil costs per aircraft or fuel per block, correlates to the dependent attribute, outcome of profitability. Correlation of these attributes as well as regression and time series to determine the relationship between the independent attributes and the dependent attribute as well as a forecast prediction of the future follows. Use of Correlation in making business decisions   “Correlation analysis is the study of the relationship between variables” (Lind, Marchal, & Wathen, 2005, p. 431). Correlation and regression analysis serve the purpose of quantifying relationships among variables, and businesses can use the results of these analyses in many ways. Correlation and regression analysis help firms develop models used to produce forecasts based on a set of business and economic indicators used as forecasts for independent variables. According to Lind, If there is a strong relationship (say, .91) between two variables, we are tempted to assume that an increase or decrease in one variable causes a change in the other variable. Relationships such as these are called spurious correlations. What we can conclude when we find two variables with a strong correlation is that there is a relationship or association between the two variables, not that a change in one causes a change in the other (p. 436). The hypothesis for the analysis is the cost of fuel oil has an impact on the airlines profitability. Therefore, the null hypothesis (H0) is the cost of fuel has no impact on airline profitability and H1 is that airline profitability has an impact on airline profitability. In other words, H0=zero if the correlation in the population is zero and H1 ≠0 if the correlation in the population is greater or less than zero (Marczyk, DeMatteo, & Festinger, 2005). Apply linear regression and correlation analysis  Table 2 shows the correlation between the total revenue and the two independent variables, fuel oil per aircraft and fuel per block. The goal is to prove there is a correlation between fuel oil and revenue and fuel per block and revenue. Given n-2 degrees of freedom or a value of five, our critical value at a 5% significance level is 2.571. Therefore, the null hypothesis is H0 = 0 when -2.571