Delta’s New Song: a Case on Cost Estimation in the Airline Industry

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Delta’s New Song:
A Case on Cost Estimation
In the Airline Industry

Contents
Question 12
Question 23
Question 34
Question 47
Question 58
Question 18
Question 29
Question 311
Question 413
Appendix 113
Appendix 219

Question 1

There are several possible factors that seem more relevant to be as a cost diver to estimate Delta’s salaries: * Available Ton Miles
* Number of Departures(thousand)
* Revenue Passenger Miles
* Revenue Ton Miles
* Revenue Miles scheduled
Salary cost for Delta consists of the payment to flight attendants and pilots so it can be determined by the hours flown. The miles and the time flown are correlated so between these cost drivers, available ton miles seems to be the most reasonable cost driver since it indicate the time that the pilots and the flight attendant work for the Delta. Question 2

We first apply simple regression using each of the cost drivers mention above and other factor to estimate the salary by the cost drivers individually to see which one is best cost driver based on statistical reason and comparing R square. The scatter plots are shown in appendix 1.

Results are as follows:

X| Y| Y=AX+B| R square|
Available Seat Miles| Salary Cost| Y=38.099X+262.71| 0.0997| Available Ton Miles| Salary Cost| Y=0.5517X-682.64| 0.5577| Number of Departures| Salary Cost| Y=-8.5728X+3184.7| 0.3229| Revenue Air Hours| Salary Cost| Y=3.2063X+112.59| 0.1239| Revenue Miles flown| Salary Cost| Y=8.0355X+21.593| 0.149| Revenue Miles Scheduled| Salary Cost| Y=8.0801X-7.3518| 0.2023| Revenue Passengers Emplaned| Salary Cost| Y=16.906X+890.46| 0.0408| Revenue Passengers Miles| Salary Cost| Y=38.238X+577.26| 0.1764| Revenue Passengers Ton Miles| Salary Cost| Y=0.3821X+577.91| 0.1765| Revenue Ton Miles| Salary Cost| Y=0.3301X+614.33| 0.1378|

We can conclude from the above table that “Available Ton Mile” is better than the other factors since it has the most R square (0.5577) and also the standard deviations are much smaller than coefficients so it’s statistically valid. On the other hand the Salary-Available Ton Miles plot shows a more linear relationship between the two variable (Salary and Available Ton Miles). Salary = 0.5517* Available Ton Miles - 682.64

| Coefficients| Standard Error|
Intercept| -682.6433471| 282.6032585|
Available Ton Miles| 0.551692525| 0.079698325|

Advantage and Disadvantage
Since this technique (single regression) uses the statistical rules to fit a function for all the historical data, it can be reliable. But we should consider that this method only use one cost driver to estimate salary cost at a time so it can not explain the whole variation for salary cost. Question 3

In this part we choose three cost drivers and use multiple regressions to estimate the salary cost. 1) Available Ton Miles
2) Number of Departure
3) Revenue Passenger Miles
We choose this cost drivers because they had the best R square in the previous part (single regression). Now we use multiple regressions to estimate the salary cost with each two of them. 1) Variable 1= Available Ton Miles, Variable 2= Number of Departure | Coefficients| Standard Error| R square|

Intercept| 1120.1809| 332.9901692| 0.798497104|
Available Ton Miles| 0.512423052| 0.05483575| |
Number of Departure| -7.445802094| 1.119799435| |

Salary Cost = 1120.1809 + 0.512423052 * Available Ton Miles - 7.445802094 * Number of Departure 2) Variable 1= Available Ton Miles, Variable 2= Revenue Passenger Miles

| Coefficients| Standard Error| R square|
Intercept| -1144.549119| 243.2101043| 0.729846407|
Available Ton Miles| 1.05193654| 0.12082857| |
Revenue Passenger Miles| -72.29550232| 14.88974415| |

Salary Cost = -1144.549119 + 1.05193654* Available Ton Miles -72.29550232 * Revenue Passenger Miles 3) Variable 1= Revenue Passenger Miles,...
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