Possible cost drivers that will allow us to estimate a salary cost function for Delta are: available seat miles, number of departures, available ton miles, revenue passenger miles, and revenue ton miles. The two cost drivers we chose were revenue passenger miles and available ton miles. The salaries consist of payments to pilots, flight attendants and ticket agents. Their salaries are determined by the number of passengers and cargoes and the miles or hours flown. This is why we chose revenue passenger miles and available ton miles. After calculation we found that the R2 of revenue passenger miles is .1764, and the R2 of available ton miles is .5577. We used scatter plots to show this:
The available ton miles scatter plot shows a more linear relationship between the two variables. Low point (3132, 1145), high point (4029, 1514) Salary=0.4114xavailable ton miles-143.50
The greatest advantage about this technique is that it only uses two data so it is convenient. The disadvantages are that the data is inefficient. This is because the data is based on cost function for only two periods, meaning it is less accurate. Simple Regression Using simpler regression to estimate the salary cost with available ton miles as the cost driver. These are the results: Coefficients Intercept X Variable 1 -682.643 0.551693 Standard deviation 282.6033 0.79698
Salary= 0.5517x available ton miles- 682.63 R2=0.5577, and the coefficients are larger than the deviations so it is valid. Regression analysis is more reliable at measuring cost behavior than other measurement methods. This is because this technique uses statistics to fit a cost function in all historical data. The regression analysis technique is an improvement compared to the high low method. It also allows analysts to pick out the best cost driver. A disadvantage with the regression analysis method is that only one cost driver is considered, so it can’t completely explain the variation of...