Is There a Correlation?Rival Airlines and Their Effect on Each Other

Justin RichmanGeography 247

Introduction

Background Airports and the airlines are important modes of

transportation in our society. On a daily basis they are used to transport varying quantities

of people and cargo over great distances in efficient timeframes

The various options of Airlines provide consumers with different ways to travel or ship goods (i.e. costs, amenities, etc)

Its has been shown that consumers will typically show loyalty to one brand of product or service that they seek to use including airlines for travel and shipping, but will also sacrifice these features to get the best overall value.

Research Question

Does the increased presence of a “discount” airline directly affect the passenger totals of a “full service/premium” from one destination to another, or do customers continue to show brand loyalty?

The focus will be on both discount airline, Airtran Airways and its increased presence at General Mitchell International Airport in Milwaukee since its failed attempt to buy local, full service airline, Midwest Airlines and its affects on 3 shared direct routes of various cities (ATL, BOS & MCO) over a 15 Month period.

Methodology

Data Collection: Research and Innovative Technology Administration:

Bureau of Transportation Statisticshttp://transtats.bts.gov/

General Mitchell International Airport http://www.mitchellairport.com/

Techniques Used: Scatter Plot Diagram(s) Correlation Pearson’s R Coefficient

Key:

YX = Midwest Airlines FL = Airtran Airways ATL=Atlanta Hartsfield Int’l Airport BOS = Logan Int’l Airport Boston, MAMCO = Orlando Int’l AirportMKE = Gen. Mitchell Field Int’l Airport Milwaukee, WI

Results

Jan-07 Apr-07 Aug-07 Nov-07 Feb-08 Jun-08 Sep-080

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

9,058

12,694

10,013

7,586

10,964

14,077

6,602 6,447

4,572 4,535

5,7544,900

MKE-ATL (YX) Linear (MKE-ATL (YX))MKE-ATL (FL) Linear (MKE-ATL (FL))

Milwaukee To Atlanta (Major Airport)

Passengers

Midwest Airlines Acquired by TPG Capital

t-Test: Paired Two Sample for Means  

   

  MKE-ATL(YX) MKE-ATL(FL)

Mean 5468.333333 10732

Variance 864424.2667 5661769.2

Observations 6 6

Pearson Correlation 0.159863868 Hypothesized Mean Difference 0 

df 5 

t Stat -5.344964021 

P(T<=t) one-tail 0.001538392 

t Critical one-tail 2.015048372 

P(T<=t) two-tail 0.003076785 

t Critical two-tail 2.570581835 

Results

Jan-07 Apr-07 Aug-07 Nov-07 Feb-08 Jun-08 Sep-080

2,000

4,000

6,000

8,000

10,000

12,000

109 163 116

1,448 1,370

6,430

8,886

11,383

7,587

6,220

9,520

11,004

MKE-BOS (YX) Linear (MKE-BOS (YX))MKE-BOS (FL) Linear (MKE-BOS (FL))

Milwaukee To Boston (Business Destination)

Passengers

t-Test: Paired Two Sample for Means  

   

  MKE-BOS(YX) MKE-BOS(FL)

Mean 9100 1606

Variance 3928614 5978998.8

Observations 6 6

Pearson Correlation 0.364017716 Hypothesized Mean Difference 0 

df 5 

t Stat 7.267886758 

P(T<=t) one-tail 0.000385429 

t Critical one-tail 2.015048372 

P(T<=t) two-tail 0.000770858 

t Critical two-tail 2.570581835 

Results

Jan-07 Apr-07 Aug-07 Nov-07 Feb-08 Jun-08 Sep-080

2,000

4,000

6,000

8,000

10,000

12,000

4,4654,943

5,623

6,319 6,409

5,707

10,514 10,2539,668 9,877

10,783

9,876

MKE-MCO (YX) Linear (MKE-MCO (YX))MKE-MCO (FL) Linear (MKE-MCO (FL))

Milwaukee To Orlando (Vacation Destination)

Passengers

t-Test: Paired Two Sample for Means  

   

 MKE-

MCO(YX)MKE-

MCO(FL)

Mean 10161.833335577.666667

Variance 184976.5667580060.2667

Observations 6 6

Pearson Correlation -0.12444586

Hypothesized Mean Difference 0 

df 5 

t Stat 12.20410627 

P(T<=t) one-tail 3.26587E-05 

t Critical one-tail 2.015048372 

P(T<=t) two-tail 6.53174E-05 

t Critical two-tail 2.570581835 

Results

Pearson’s R Values

MKE-ATL 0.16 since the value between 1 &0 it shows a positive correlation

MKE – BOS 0.37 since the value between 1 &0 it shows a positive correlation

MKE – MCO -0.12 the correlation is negative, but when looking at the liner regression lines you’ll notice a trend that may lead to a neutral/positive correlation over time

Months YX MKE-ATL FL MKE-ATL YX MKE-BOS FL MKE-BOS YX MKE-MCO FL MKE-MCOMay-07 6,602 9,058 8,886 109 10,514 4,465Aug-07 6,447 12,694 11,383 163 10,253 4,943Nov-07 4,572 10,013 7,587 116 9,668 5,623Feb-08 4,535 7,586 6,220 1,448 9,877 6,319May-08 5,754 10,964 9,520 1,370 10,783 6,409Aug-08 4,900 14,077 11,004 6,430 9,876 5,707Totals 32,810 64,392 54,600 9,636 60,971 33,466Mean 5,468 10,732 9,100 1,606 10,162 5,578

Flights YX MKE-ATL FL MKE-ATL YX MKE-BOS FL MKE-BOS YX MKE-MCO FL MKE-MCOYX MKE-ATL 1  FL MKE-ATL 0.159863868 1  

YX MKE-BOS 0.55064675 0.90971349 1  FL MKE-BOS -0.390195309 0.595533033 0.364017716 1  

YX MKE-MCO 0.768664658 0.005568247 0.332428468 -0.266891569 1  FL MKE-MCO -0.692290533 -0.123820238 -0.373488164 0.303335717 -0.124445861 1

Conclusions

Overall the increased presence of a Airtran Airways seems to directly affect the total number of passengers flying Midwest Airlines to the same destinations

Milwaukee to Atlanta & Milwaukee to Boston flights show the highest correlation of Airtran’s influence while its to early to tell if the same will happen to the Milwaukee to Orlando routes.