Quantitative Analysis for Management

QuantitativeAnalysis for Management

Accordingto the case study of the amalgamation of the two airlines, Northern,and Southern Airline, which formed the largest U.S. carrier, theproblem statement depicts that the maintenance cost rises as theaircrafts ages. Mr. Stephen distress is the high maintenance cost ofthe aircraft, B737-200, which previous information regarding theindustry shows that the maintenance cost increases as the aircraftages. Therefore, the main objectives of the study are outline asfollows:

Thestudy seeks to examine the correlation between the average fleet agesand direct airframe maintenance cost which was incurred in differentyears.

Secondly,it examines the relationship between the average fleet age and directengine maintenance cost.

Thecorrelation coefficient in statistics refers to the measure ofassociation between two variables (Mari 2001). From the study, weshall examine the Pearson correlation coefficient between the averagefleet ages and direct engine maintenance cost. Northern and Southernairline have the correlation coefficients of 0.8772 and 0.6249respectively. The Northern Airline shows a strong linear correlationbetween the average fleet age and the airframe maintenance cost ascompared to the Southern Airline which is moderately positive(Table1.1 and 2.1). This shows that there is a relatively positiverelationship among the two variables. This relationship depicts thedirection of the association between the two variables as delineatedby the positive figure that show the degree of association.

Additionally,the study focused on examining the relationship between the averagefleet age and the direct engine cost. To effectively show thisrelationship, statistical regression analysis is applied in order toportray and capture the association amongst the exogenous andendogenous variables. Regression analysis refers the statistical toolused for investigation of the relationship between two or morevariables. The main focus of regression is primarily for predictionand causal inference (Campbell et al. 2008). From the data provided,we shall be looking whether the average fleet age influences theengine maintenance cost over the period of time. The regressionanalysis shows that a one unit change in the average fleet age ofboth the Northern and the Southern airline will increase enginemaintenance cost by 231.70 and 113.13 respectively (Table 2.1 and2.2). This shows that there is a causal effect from the average fleetage which causes the maintenance cost to increase. However, we cannotcompletely term the increase in the maintenance cost to be purelydepend on the aging fleet. It is a combination of the aging fleet andother factors which are present in the airline industry. Thus, thisbrings us to the interpretation of the adjusted R2.This shows the percentage variation caused by the explanatoryvariable. The adjusted R2of Northern Airline shows that aging fleet accounts for 53.49% of thetotal variation of the maintenance cost leaving about 46.51% of thevariation explained by other factors. On the other hand, the adjustedR2of the Southern Airline show that 35.19% of the variation arecontributed by the aging fleet and the remaining 64.81% of thevariation are contributed by other factors.

Conclusion

Fromthe study, there is a strong linear correlation relationship betweenthe direct airframe maintenance cost and the average fleet age inboth locations. On the other hand, the study confirms that averagefleet age contributes 53.49% and 35.19% of the variation in directengine costs while the rest of the variation is explained by otherfactors present in the industry in Northern and Southern Airline.

Northern Airline

Pearson Correlation Coefficients

&nbsp

Year

Airframe Cost

Engine Cost

Average Age (hours)

Year

1.0000

&nbsp

&nbsp

Airframe Cost

0.8721

1.0000

&nbsp

&nbsp

Engine Cost

0.7150

0.6360

1.0000

&nbsp

Average Age (hours)

0.9887

0.8772

0.7826

1.0000

Fig 1.1

Southern Airline

Pearson Correlation Coefficients

&nbsp

Year

Airframe

Engine Cost

Average Age (hours)

Year

1.0000

&nbsp

&nbsp

Airframe Cost

0.7673

1.0000

&nbsp

&nbsp

Engine Cost

0.3398

0.6826

1.0000

&nbsp

Average Age (hours)

0.6110

0.6249

0.6782

1.0000

Fig1.2

Fig2.1 Northern Airline

Regression Statistics

Multiple R

0.782592494

R Square

0.612451011

Adjusted R Square

0.534941213

Standard Error

2738.098181

Observations

7

ANOVA

&nbsp

df

SS

MS

F

Regression

1

59239691.18

59239691

7.901595

Residual

5

37485908.25

7497182

Total

6

96725599.43

&nbsp

&nbsp

&nbsp

Coefficients

Standard Error

t Stat

P-value

Intercept

-82.98818591

4450.483976

-0.01865

0.985844

Engine (X)

231.694552

82.4249032

2.810978

0.037505

Fig2.2 Southern Airline

Regression Statistics

Multiple R

0.678175096

R Square

0.459921461

Adjusted R Square

0.351905754

Standard Error

1187.740833

Observations

7

ANOVA

&nbsp

df

SS

MS

F

Regression

1

6006757.991

6006757.99

4.257913

Residual

5

7053641.437

1410728.29

Total

6

13060399.43

&nbsp

&nbsp

&nbsp

Coefficients

Standard Error

t Stat

P-value

Intercept

4022.943093

1654.223222

2.43192275

0.05924

Engine (X)

113.131633

54.82588816

2.06347105

0.094017

Reference

Schroeder,L., &amp Sjoquist, D. (1986). Understanding regression analysis anintroductory guide. Beverly Hills, Calif.: Sage Publications.

Introductionto Regression and Data Analysis – StatLab. (n.d.). Retrieved August15, 2015.&nbsp

Campbell,D., &amp Kenny, D. (1999).&nbspA primer on regression artifacts.New York: Guilford Press.&nbsp

Mari,D., &amp Kotz, S. (2001).&nbspCorrelation and dependence. London:Imperial College Press&nbsp