cheungalley.files.wordpress.com · Web viewIn 2011, Los Angeles International Airport (LAX) ranked...

Running head: LAX-TOM BRADLEY TERMINAL QUANTITATIVE REPORT 1

LAX Tom Bradley Terminal Quantitative Report

Woodbury University

Zenaida A. Barredo

Alley Cheung

Arsen Der-Aphrahamian

Lucy Harutyunyan

Lernik Rostami

Gevik Shahverdi

WMBA 503

Dr. Michel Cook

December 6, 2012

LAX – TOM BRADLEY TERMINAL QUANTITATIVE REPORT 2

Table of Contents

Introduction..................................................................................................................................3

Methodology................................................................................................................................4

Analysis........................................................................................................................................6

Conclusion.................................................................................................................................13

Recommendations......................................................................................................................14

Appendixes.................................................................................................................................16


LAX TOM BRADLEY QUANTITATIVE REPORT

Introduction

In 2011, Los Angeles International Airport (LAX) ranked 6th on the busiest airport

worldwide list (ACI, 2011), serving the second most populated metropolitan area in the United

States (U.S. Census Bureau, 2010). The report released by Los Angeles World Airports (LAWA)

in January of this year further supports the ranking with 61,862,052 LAX passengers in 2011 of

which, 27.05 % accounts for international travelers. Additionally, the report showed a 4.9

percent growth on International traffic compared to 4.6 percent growth in domestic passengers.

With the increase in passenger traffic and aging facilities, LAX officials had been

working on updating the airport by looking at various opportunities for modernization and

expansion. However, limited space, economic downturn, community indifference, environmental

impacts, and safety concerns are some of the many challenges that airport officials need to

address in order to improve service, expand airport space, meet passenger expectations for

innovative and modernized airport facility. Collaborative efforts with various stakeholders and

project studies are being conducted with the primary objective of meeting increasing demand for

an updated, more organized and less congested airport.

Utilizing the data provided by LAWA and the observations gathered by the group

members, this report will attempt to find some explanations as to where and why there is

congestion, and what types of vehicles create most traffic and or takes most time to load/unload

passengers. Additionally, the numbers and types of vehicles stopping and passing by, why some

travelers prefer taxi cabs over other modes of transportation, how their choices of transportations

are affected by the purpose of their trips, and most importantly the reasons why Tom Bradley

International was the terminal choice of the team to focus on.


Methodology

The first step taken by the group was to choose the terminal at LAX that services most

number of airline carrier. This information was extracted from the LAWA 2011 report posted on

its website. Data showed that Tom Bradley International Airport terminal ranked first with 31

carriers and accounts for 8,635,397 out of the total 16,731,324 (equivalent to 51.61 %)

international passengers in 2011. It was therefore decided that Tom Bradley would be the choice

to focus on. The group members then proceeded to gather observations through survey

instruments in paper and oral interviews and ocular observations. The loading and unloading

time for taxi, shuttles and private cars were taken, the number and type of cars stopping and

passing by Tom Bradley terminal were counted. Paper and oral surveys were also conducted.

The lengths of loading and unloading time of vehicles were observed on November 11, 2012,

between 16:00 to 17:00, and on November 16, 2012, between 10:00 to 11:00, and on November

18, 2012, between 10:00 to 12:00. The data collected were translated to statistically with the use

of different techniques learned in the class such as the Binomial distribution, ANOVA test, F-test

and Inferences about Pairs of Treatment Means. The results and analyses of the test results are

presented in narrative, charts, tables, and box plots.

Our hypothesis is that larger group of travelers tend to take other types of transportation

such as private vehicles, shuttle, or limo and that smaller group of travelers such as a single

person traveling alone or with one other person would take a cab. Essentially we want to know

the number of people in any given cab ride.

A key question we seek to answer is whether or not all taxis leave LAX with at least one

passenger riding in it. Also, what is the likelihood that a party of two or more would not take a

cab ride and would instead take a shuttle or private vehicle or any other forms of transportation?


Furthermore, in terms of capacity, we seek to find the number of vehicles coming through LAX

and if the average passenger per vehicle that leaves LAX is 3 to 5.

The way we set up our data points are in three categories. Our original data observes 50

random samples of vehicles making a stopping at the departure terminal of Tom Bradley to drop

off travelers. Of those 50 vehicles, we classified them as taxi and non-taxi. The non-taxi category

includes private vehicles, shuttle, limos, rental cars, etc. Furthermore, a survey was conducted

with the following questions on each observation:

1. Are you a local to Los Angeles or are you a visitor to Los Angeles? 2. Are you

traveling on business of for leisure? Finally, each observation has a travel party size to aid in our

hypothesis of travel size effect on choice of transportation.

Each taxi that leaves the airport is reportedly leaving with at least one passenger. We did

our study based on an assumption that shuttle services are for larger parties and those who did

not take a taxi, meaning that they were dropped off by a family or friends personal vehicle, used

a shuttle, or limo services, seeking to find the outcome that travel party size of these travelers

were different.

To test our hypothesis, we conducted an analysis of variance to compare the average

mean of one category to the mean of another category. The level of significance used for these

tests were .05 and consistency for all three tests. This means that the analysis of variance is under

the assumption that it is the same average. For example, if the average people in a taxi are two,

than we assume whether they take a taxi or not. What we are assuming is that the population is

the same unless we get results that show otherwise. For this reason assume the same average and


same standard deviation. Our conclusion will be based on if they are far enough apart that we can

say that they are different.

Analysis

ANOVA Analysis on party size taking taxi cab

Our findings show that there is no significant difference between party size of people that

took a cab to the Tom Bradley airport to catch their departure flight and the people that chose

some other form of transportations (shuttle or was dropped off by a family member). The level of

significance is calculated on a .05 level. The analysis gave an F statistic of 0.48 with a F critical

of 4.04. Since the F statistic is less than F critical we fail to reject the null hypothesis, which

means that the variance is insignificant and could be a result of sampling error. Furthermore, this

means that it is so insignificant that the size of the party does not influence decision on what

form of transportation a traveler will chose to arrive at the Tom Bradley international terminal to

catch a departure flight.

ANOVA Analysis on passenger profiles

In looking at Tom Bradley departures on Sunday, November 18 between the hours of

10am and 12pm with a sample size of 50, we observed that 34 took a taxi and 33 took some

other form of transportation in getting to the airport to catch their departure flights. Of the

travelers who were locals and the travelers who were visitors, those choosing to leave LAX, their

party size was not much difference than those who did not take a cab as transportation.

Similarly, the level of significance is calculated on a .05 level, this analysis shows that

the calculated alpha is much higher. The analysis gave an F statistic of 0.004 with a F critical of

3.98. Since the F statistic is less than F critical we fail to reject the null hypothesis, which means

that the variance is insignificant and could be a result of sampling error. Furthermore, this means


that it is so insignificant that the size of the party doesn’t influence decision on what form of

transportation a traveler will chose to arrive at the Tom Bradley international terminal to catch a

departure flight regardless is if they are local to Los Angeles or if they were visiting Los

Angeles.

ANOVA Analysis on passenger profiles and passenger counts

In looking at Tom Bradley departures on Sunday, November 18 between the hours of

10am and 12pm with a sample size of 50, we observed that 19 people were on business trips

arriving with a taxi and 31 were on vacation arrived with another form of transportation in

getting to the airport to catch their departure flights.

Similarly, the level of significance is calculated on a .05 level, this analysis shows that

the calculated alpha is much higher. Here we observe a slightly different outcome. Since the

difference between the F statistic 3.96 and F-Critical 4.04 is very close, normal research would

say that we have a 4.0 F-Critical and even though it is not significant, although it does have a

very close F statistic. Finally, since the data looks really close, we have to conclude that there is

no difference in significance between the count of people arriving at this terminal with taxi who

are business travelers and the count of people that are on vacation and arrived with a different

form of transportation.

Moreover, 251 random samples were taken at the arrival and departure area to test which

type of vehicles takes most time in unloading and loading passengers. The first group of

observations were collected from the departure area and statistically tested.

Unloading time at the departure area - Tom Bradley International Airport Terminal


The result of the ANOVA test applied on the unloading time for three groups namely:

taxis, shuttles and private cars at the departure area of the Tom Bradley airport terminal

demonstrated that there is a significant difference in the unloading time between the three groups

as indicated by a lower F-Critical at 3.077 compared to the F-value of 9.23. This conclusion is

further supported by the mean averages of 87.84, 47.79 and 147.03 for taxis, shuttles and private

cars respectively. The table below further shows the variance, degree of freedom and the P-

value:

Analysis on unloading time at the departure area of Tom Bradley Terminal

ANOVA: Single Factor

SUMMARY

Groups Count Sum Average Variance

Taxi 44 386587.8409

1 3429.579

Shuttles 33 157747.7878

8 1243.047

Private cars 38 5587147.026

3 24262.51

ANOVASource of Variation SS df MS F P-value F crit

Between Groups178573.

6 289286.7

9 9.230.00019

7 3.077

Within Groups 1084962 1129687.16

4

Total 1263536 114


With the same collected data, the observations for all three groups are

also presented in the box plot format as shown below:

Taxi

Shuttle

Private

0 100 200 300 400 500 600 700 800

ZeroMin - Q1Q 1 - MedianMedian - Q2

Box plot comparison of 115 observations of loading and unloading times for Taxis, shuttles and POVs in the Departure area

The box plots above shows that there is very minimal overlap or similarity of the three

probability distributions for the three categories of Private, Shuttle and Taxi. Significantly, 25

percent of the Private vehicles take much longer time to load or unload their passengers’

belongings, up to 13 minutes. Same kind of trend is also seen with the other two vehicles, but not

as significantly high as for private vehicles. Other than that, the probability distribution seems to

be relatively normal.

To further explain the observations, the result on the inference on pairs of treatment

showed that at 95 percent level of confidence, the end points of the confidence interval are 63.25

and 55.12 for taxis and private cars. Since both end points are positive or skewed to the right, it

can be concluded that the treatment means between private cars and taxis differ significantly that

is, the unloading time at the departure area for taxis measures differently from those of private

cars.


Comparatively, with 95 percent level of confidence, the end points of the confidence

interval are 43.92 and 46.19 for taxi and shuttles. Since both end points are positive or skewed to

the right, it can be concluded that the treatment means between shuttles and taxis differ

significantly, that is, the unloading time at the departure area for shuttles measures differently

from those of taxis.

Furthermore, with 95 percent level of confidence, the end points of the confidence

interval are 103.23 and 95.25 for shuttles and private cars. Since both end points are positive or

skewed to the right, it can be concluded that the treatment means between shuttles and private

cars differ significantly that is, the unloading time at the departure area for shuttles measures

differently from those of private cars.

Similarly, the table below illustrates the differences between the three groups with private

cars having the highest mean at 147.03 compared to shuttles at 47.78 and taxis at 87.84.

Furthermore, private car skewness is at 2.68, which is nearly double compared to the taxis and

shuttles at 1.21 and 1.36 respectively.

Taxi Shuttles Private cars

Mean 87.84090909 Mean 47.78787879 Mean 147.0263158Standard Error 8.82864495 Standard Error 6.137434437 Standard Error 25.26830384Median 75 Median 52 Median 94.5Mode 57 Mode 57 Mode #N/AStandard Deviation 58.56260541

Standard Deviation 35.25687661

Standard Deviation 155.764286

Sample Variance 3429.578753 Sample Variance 1243.047348 Sample Variance 24262.5128Kurtosis 1.414977439 Kurtosis 3.138063738 Kurtosis 7.462234716Skewness 1.212438594 Skewness 1.356461688 Skewness 2.682513604Range 244 Range 165 Range 711Minimum 13 Minimum 6 Minimum 27Maximum 257 Maximum 171 Maximum 738Sum 3865 Sum 1577 Sum 5587Count 44 Count 33 Count 38


Loading time at the arrival area - Tom Bradley International Airport Terminal

The ANOVA test conducted for the loading time at the arrival area demonstrated

similarly and that there is a significant difference in the loading time between the three groups

as indicated by a lower F-Critical at 3.91 compared to the F-value of 27.17 or can be expressed

as that the F-value is 3 times more than the F-Critical. This conclusion is further supported by

the mean averages of 44.43 and 113.86 for taxis and private cars respectively. The table below

further shows the variance, degree of freedom and the P-value:

Analysis on loading time at the arrival area of Tom Bradley Terminal

ANOVA: Single Factor

SUMMARYGroups Count Sum Average Variance

Taxi 60 2666 44.43 737.33Private cars 76 8653 113.86 10045.41


Between Groups161591.

8 1 161591.8 27.171646.88E-

07 3.911795

Within Groups796908.

1 134 5947.076

Total958499.

9 135


The box plot the follows further explains the observations gathered:

Taxi

Private

0 100 200 300 400 500 600 700

ZeroMin - Q1Q 1 - MedianMedian - Q2

Box plot comparison of 137 observations of loading and unloading times for Taxis and POVs in the Arrival area

The box plots above shows that the observations in the arrival area follow the same

trends as the departure area; i.e. 25 percent of the private vehicles and taxis take a much longer

time to load and unload. This difference in timing is more significantly notices for private

vehicles, taking up to 10 minutes to load or unload.

The inference on the pairs of treatment means further support the findings that at 95

percent level of confidence, the end points of the confidence interval are 18.29 and 13.71 for

taxis and private cars. Since both end points are positive or skewed to the right, it can be

concluded that the treatment means between private cars and taxis differ significantly that is, the

loading time at the arrival area for taxis measures differently from those of private cars.

Lastly, the data below further supports that the probability of a private car stopping at

Tom Bradley International terminal for unloading and unloading purposes is 71.19 % compared

to 22.03 % for shuttles and taxis at only 6.78 % likelihood of dropping off and loading

passengers.


Probabilities of Vehicles Stopping By at Tom Bradley Terminal

p(x)Taxi 12 0.0678Shuttles 39 0.2203Private cars 126 0.7119

177 1.00

The same observation is depicted for vehicles passing by at the same terminal.

Probabilities of Vehicles Passing by at Tom Bradley Terminal

p(x)Taxi 66 0.0530Shuttles 129 0.1036Private cars 1050 0.8434

1245 1.00

The Binomial distribution results depicted in tables 1-5 and graphs 3-7 in the Appendix

support the analyses discussed above. The distributions were divided into three categories: length

of unloading and loading time for taxis, shuttles and private cars for a minute or less, 1.1 minutes

to 2 minutes, and over 2 minutes. As shown in the results, private cars ranked the first in taking

the over two minutes unload or unload passengers and their belongings. There is a probability of

25 percent that 4 out of 10 private cars will stop for longer than 2 minutes in the unloading area,

and a probability of 27 percent that 3 out of 10 private cars will stop for longer than 2 minutes in

the loading area. The probabilities of 4 out of 10 other vehicles to spend longer than 2 minutes in

the unloading or loading areas are relatively low, when comparing with those of private cars. On

the departure level, it is most possible to have 2 out of 10 randomly selected taxi cabs to take

longer than 2 minutes, while we have 73.5% confidence that no shuttles will spend over 2


minutes to unload. On the arrival level, similar observations can be drawn, as most of the taxi

cabs will not spend over 2 minutes to load passengers, with a probability of 84.5%.

ConclusionNo conclusion can be drawn as to explain why travelers in our sample size chose a taxi as a

form of transportation to LAX. We only excluded these three reasons that they were locals and

or visitors and if they were traveling on business or for leisure and whether they arrived at the

terminal with a cab ride or another form of transportation. Therefore, we failed to disprove if

there is a relationship between categories. We can conclude that it does not matter if travelers

were locals of visitor, traveling on business or leisure.

It was observed that regardless of the time and of the day, private cars takes most of the time

loading and unloading passengers at Tom Bradley International terminal and applies to both

departure and arrival areas but noticeably private cars taking longer to load than unloading

passengers. The significance of the finding is that the traffic congestion is mainly caused by

private cars instead of taxis, which was previously assumed based on the LAWA. The

assumption was made based on the significant number of taxis coming in and out of LAX.

RecommendationsIn a short term, the following recommendations may help alleviate the traffic congestions at

LAX:

1. Increase uniformed personnel presence at the Tom Bradley terminal during peak hours.

2. Limit the loading and unloading time at curbside to 2 minutes.

3. Strictly enforce traffic regulations.

4. Set up electronic signs to remind drivers of the regulations and time limits for loading and

unloading and to notify drivers of the traffic conditions for each terminal.


5. Send out traffic alerts within LAX

6. Provide alternate location for passengers in tour groups.

7. Provide free parking for the first 15 minutes in order to reduce number of vehicles loading

and unloading passengers at curbside.

References

Airports Council International (2011). Statistics. Retrieved from http://www.aci.aero/

Lind, D. A., Marchal, W.G, Wathen, S. A. (2012). Statistical Techniques in Business and

Economics. New York, NY: The McGraw-Hill Companies Inc.

Los Angeles World Airports (2012). Los Angeles International Airport Reports 2011 Passenger

Level Up 4.7 Percent Over 2010; Air Cargo Down 3.8 Percent. Retrieved from

http://www.lawa.org/newsContent.

U.S. Census Bureau. (2010). U.S. Census Bureau Releases Data on Population Distribution and

Change in the U.S. Based on Analysis of 2010 Census Results. U.S. Census Bureau.

March 24, 2010. Retrieved from http://2010.census.gov/news/releases/operations/cb11-

cn124.html.


AppendixesGraph 1

Comparison of 50 observations based on three different categories

1. Passengers (groups) who took a taxi vs. passengers who took other means of

transportation

2. Local passenger (groups) vs. visitors

3. Business travelers vs. leisure travelers (count of parties)

Taxi other Locals Visitors Business Vacation 0

5

10

15

20

25

30

35

Series1


Graph 2

Box plot comparison of 50 observations based on three different categories

1. Passengers (groups) who took a taxi vs. passengers who took other means of

transportation

2. Local passenger (groups) vs. visitors

3. Business travelers vs. leisure travelers (count of parties)

taxi

other

locals

visitors

business trip

vacation

0 1 2 3 4 5 6 7

ZeroMin - Q1Q1 - MedianMedian - Q2


Table 1Binomial Distribution of vehicles taking 1 minute or less for loading/unloading

TAXI CABS PRIVATE CARS SHUTTLES

# of Success Departure Arrival Departure Arrival Departure Arrival

0 0.010897 0.00000004 0.09408521 0.015189 0.0000071 0.062258 0.00000191 0.25085416 0.078985 0.0001502 0.160067 0.00003825 0.30097733 0.184829 0.0015533 0.243873 0.00045447 0.21399444 0.256302 0.0095274 0.243835 0.00354360 0.09984826 0.233239 0.038354 Insufficient5 0.167175 0.01894635 0.03194638 0.145545 0.105871 sample data6 0.079595 0.07034682 0.00709807 0.063071 0.2029497 0.025986 0.17910451 0.00108144 0.018741 0.2667718 0.005568 0.29925257 0.00010813 0.003655 0.2301239 0.000707 0.29629578 0.00000641 0.000422 0.117635

10 0.000040 0.13201569 0.00000017 0.000022 0.0270601.00 1.00 1.00 1.00 1.00

Graph 3Probabilities of Vehicles to take 0.1-1minute to load/unload at Tom Bradley Terminal

1 2 3 4 5 6 7 8 9 10 110.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

TAXI CABS Departure TAXI CABS Arrival PRIVATE CARS Departure PRIVATE CARS Arrival SHUTTLES Depar-ture


Table 2Binomial Distribution of vehicles taking 1.1 – 2minutes for loading/unloading at Tom Bradley Terminal

TAXI CABS PRIVATE CARS

SHUTTLES


0 0.007566 0.1614409923 0.00422703 0.012412 0.0414131 0.047644 0.3229594793 0.03074802 0.068392 0.1552772 0.135011 0.2907332934 0.10064945 0.169583 0.2619943 0.226720 0.1550949719 0.19523686 0.249180 0.2619584 0.249850 0.0542962686 0.24853157 0.240277 0.171886

5 0.188805 0.0130342321 0.21694243 0.158875 0.077338insufficien

t

6 0.099079 0.0021728934 0.13150581 0.072952 0.024165sample

data7 0.035653 0.0002483903 0.05466239 0.022970 0.0051778 0.008419 0.0000186337 0.01491082 0.004746 0.0007289 0.001178 0.0000008284 0.00241030 0.000581 0.000061

10 0.000074 0.0000000166 0.00017533 0.000032 0.0000021.00 1.00 1.00 1.00 1.00

Graph 4Probabilities of Vehicles to take 1.1-2 minutes to load/unload at Tom Bradley Terminal

1 2 3 4 5 6 7 8 9 10 110.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

TAXI CABS Departure TAXI CABS Arrival PRIVATE CARS Departure PRIVATE CARS Arrival SHUTTLES Departure


Table 3Binomial Distribution of vehicles taking > 2minutess for loading/unloading at Tom Bradley Terminal

TAXI CABS PRIVATE CARS SHUTTLES


0 0.0563140.84500716534705000

0 0.01010232 0.027204 0.7351466052631220

1 0.1877120.14351286139830900

0 0.05892488 0.118054 0.2297096229707400

2 0.2815680.01096815980278950

0 0.15466382 0.230538 0.0322995845024857

3 0.2502820.00049674434714826

9 0.24056639 0.266784 0.0026913544681869

4 0.1459980.00001476391085671

5 0.24555598 0.202603 0.0001471682692334 insufficient

5 0.0583990.00000030089369833

1 0.17187363 0.105506 0.0000055182409707 sample data

6 0.0162220.00000000425855517

3 0.08354212 0.038154 0.0000001436893725

7 0.0030900.00000000004132897

8 0.02784486 0.009461 0.0000000025656170

8 0.0003860.00000000000026321

8 0.00609051 0.001540 0.0000000000300627

9 0.0000290.00000000000000099

3 0.00078944 0.000148 0.0000000000002087

10 0.0000010.00000000000000000

2 0.00004605 0.000006 0.0000000000000007

1.00 1.00 1.00 1.00 1.00


Graph 5Probabilities of Vehicles to take >2 minutes to load/unload at Tom Bradley Terminal

1 2 3 4 5 6 7 8 9 10 110.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

TAXI CABS Depar-ture TAXI CABS Arrival PRIVATE CARS Departure PRIVATE CARS Arrival SHUTTLES Departure

Table 4Probabilities of vehicles stopping by at the departure level of Tom Bradley Terminal

# of Success Taxi cabs Shuttles Private cars

0 0.1793230.0940852

1 0.000046

1 0.3362440.2508541

6 0.000789

2 0.2837170.3009773

3 0.006091

3 0.1418640.2139944

4 0.027845

4 0.0465510.0998482

6 0.083542

5 0.0104740.0319463

8 0.171874

6 0.0016370.0070980

7 0.245556

7 0.0001750.0010814

4 0.240566

8 0.0000120.0001081

3 0.154664

9 0.0000010.0000064

1 0.05892510 0.000000 0.0000001 0.010102


71.00 1.00 1.00

Graph 6Probabilities of vehicles stopping by at the departure level of Tom Bradley Terminal

1 2 3 4 5 6 7 8 9 10 110.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Taxi cabsShuttlesPrivate cars

Table 5Probabilities of vehicles passing by at the departure level of Tom Bradley Terminal

# of Success Taxi cabs Shuttles Private cars0 0.580096 0.33497969 0.000000011 0.324658 0.38714743 0.000000482 0.081764 0.20134776 0.000011583 0.012203 0.06205452 0.000166274 0.001195 0.01255074 0.001567135 0.000080 0.00174064 0.010128086 0.000004 0.00016764 0.045455657 0.000000 0.00001107 0.139891628 0.000000 0.00000048 0.282530159 0.000000 0.00000001 0.3381381210 0.000000 0.00000000 0.18211091


1.00 1.00 1.00

Graph 7Probabilities of vehicles passing by at the departure level of Tom Bradley Terminal

1 2 3 4 5 6 7 8 9 10 110.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Taxi cabsShuttlesPrivate cars

Table 6

Analysis of Variance between people who took a taxi vs. people who did not (Based on a survey with passengers at Tom Bradley)

SUMMARY

Groups CountSum Average

Variance

count of ppl took taxi 21 592.809523

81.26190

5

count of ppl did not take taxi 29 742.551724

11.97044

3


Between Groups 0.80949 1 0.809491 0.48321 0.49032 4.04265


1 5 1 2

Within Groups80.4105

1 481.675218

9

Total 81.22 49

Table 7

F-test between people who took a taxi vs. people who did not (Based on a survey with passengers at Tom Bradley)

count of ppl took taxi count of ppl did not take taxiMean 2.80952381 2.551724138Variance 1.261904762 1.97044335Observations 21 29df 20 28F 0.640416667P(F<=f) one-tail 0.152699855F Critical one-tail 0.487410359


Table 8

Analysis of Variance between local people vs. visitors(Based on a survey with passengers at Tom Bradley)

SUMMARYGroups Count Sum Average Variance

count of ppl local 34 84 2.470588235 0.923351159count of ppl visitors 33 81 2.454545455 0.943181818


Between Groups 0.00431 1 0.004310001 0.004618944 0.946024 3.98856Within Groups 60.65241 65 0.933113945

Total 60.65672 66

Table 9


count of ppl local count of ppl visitorsMean 2.470588235 2.454545455Variance 0.923351159 0.943181818Observations 34 33df 33 32F 0.978974722P(F<=f) one-tail 0.475317634F Critical one-tail 0.557590967


Table 10

Analysis of Variance between people on business trip vs. people on vacation(Based on a survey with passengers at Tom Bradley)

SUMMARY

Groups CountSum Average

Variance

count of ppl on business 19 422.21052

60.50877

2

count of ppl on vaca 31 912.93548

42.19569

9


Between Groups6.19113

8 16.19113

83.96080

40.05228

34.04265

2

Within Groups75.0288

6 481.56310

1

Total 81.22 49

Table 11


count of ppl on business count of ppl on vacaMean 2.210526316 2.935483871Variance 0.50877193 2.195698925Observations 19 31df 18 30F 0.231712975P(F<=f) one-tail 0.000978214F Critical one-tail 0.474576176

cheungalley.files.wordpress.com · Web viewIn 2011, Los Angeles International Airport (LAX) ranked...

Documents

Transcript of cheungalley.files.wordpress.com · Web viewIn 2011, Los Angeles International Airport (LAX) ranked...