RSCH 665 ERAUDB Flight Delays in Developed Countries American Airline Case Study

  • Title page
  • An Abstract
  • A Brief Introduction.Be sure to clearly state why the data analysis you performed is important to the aerospace/aviation/industry community, or any discipline that you have interest in. What could be learned from your analysis that could improve understanding, safety, etc.? Summarize this section within 2 – 3 paragraphs.
  • A Brief Literature Review.Collect a minimum of four (4) references from the Hunt Library on the subject related to your data. For example, if you analyzed accident data, search for research articles related to the types of accidents you analyzed. References should be from scholarly journals. If you cannot find articles related to your data, consult with the course instructor. Discuss each paper and how it relates to your data analysis. What have researchers found in the subject area? What conclusions did they make? How might this relate to your current analysis? Summarize this section within 2 – 3 pages.
  • Review Methods you usedWhat method of inquiry did you use? What data did you use? Where/how did you collect it? How did you process the data? What statistical test did you use? Why did you use it? How did you ensure the data met the assumptions of the test? Discuss sample size and power. Summarize this section within 2 – 3 paragraphs.
  • ResultsDiscuss what you found. What were the results of your statistical analysis (note: DO NOT include this in the review of methods’ section)? Were the findings significant or not? What post-test or additional tests did you conduct (if applicable)? Summarize this section within 2 – 3 paragraphs.
  • ConclusionsDiscuss how your findings relate to previous research. Are your findings different? The same? This is where you can make comments about how your findings can improve understanding of the subject. It is important to synthesize your findings and the materials you found in the literature review. What are the implications of your findings? Summarize this section at a minimum of 1 page.
  • AppendicesProvide all charts and graphs. Be sure to label in APA format. You should refer to these within the body of the document (above sections).
  • Reference listList all references in APA format. Don’t forget to include APA formatted in-text citations throughout the document. For additional assistance with APA formatting, review the following: APA In-text Citations (Links to an external site.)APA References (Links to an external site.)Headings (Links to an external site.)APA Tables (Links to an external site.)APA Figures

Title Page
RSCH 665
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Final Project Example
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Abstract
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RSCH 665
Final Project Example
Introduction
Salaries of professionals in the United States are based on a variety of characteristics of
the individual professional. The skill level of the professional should be the primary reason that
individuals differ in compensation. It has long been accepted that the cost of living in the
geographic area also impacts the salary of those employed in the area. This study has been
planned to determine if the salary of accountants is different if the employee lives in the north,
south, central or west part of the United States.
Literature Review
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Put your literature review here.
Research Methods
On order to investigate the impact of geographic area on the salary of professional
accountants, salary data from a data base was used (citation for the data base). The causal
comparative study examined one independent variable and one dependent variable.
Geographic location served as the independent variable. The researcher was unable to
manipulate the independent variable resulting in the study as causal comparative.
Each state was classified as a northern, southern, central or western state based on their
geographic location. Table 1 shows the states in each geographic area. An effort was made to
divide the states equally between the four geographic areas.
Table 1: Classification of States
Southern States
Arkansas
Alabama
Florida
Georgia
Kentucky
Louisiana
Mississippi
North Carolina
South Carolina
Tennessee
Virginia
West Virginia
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Northern States
Connecticut
Delaware
Maryland
Pennsylvania
Maine
Massachusetts
New Hampshire
New Jersey
New York
Ohio
Rhode Island
Vermont
Washington DC
Central States
Idaho
Illinois
Iowa
Kansas
Minnesota
Michigan
Missouri
Nebraska
Oklahoma
North Dakota
South Dakota
Texas
Wisconsin
Western States
Alaska
Arizona
California
Colorado
Hawaii
Montana
Nevada
New Mexico
Oregon
Washington
Utah
Wyoming
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The mean salary for accountants from each state served as the dependent variable. The
data was not collected by the researcher but there is no reason to question the quality of the
data. The number of accountants that were sampled in each state are unknown. A power
analysis will be conducted if the statistical test does not find any differences. The unit of
analysis for this study was the state, therefore, increasing the number of cases will not be
possible to increase insufficient power.
The mean salaries for each geographic area are shown in Table 2.
Table 2: Mean Accountant Salaries
n
13
13
12
12
Mean
41,821.69
46,487.85
41,034.92
43,638.75
Standard Deviation
815.92
835.09
749.12
1379.45
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Geographic Area
Central
North
South
West
The null hypothesis for this study was Ho: μ1 = μ2 = μ3 = μ4 . It was anticipated that there
would be significant differences in the salaries. A One-Way ANOVA was used with a level of
significance equal to .05 to compare the mean salaries of the four geographic groups.
Results
The salary for each group was compared using a One-Way ANOVA. The independent
variable in the study was the geographic area and the dependent variable was the mean state
salary for accountants. As shown in Table 3, significant differences were found in the salary of
accountants based on the geographic location (F=6.36, p=.0011). These results can be
interpreted to mean that at least one geographic location had a significantly different salary.
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Table 3: ANOVA Analysis of State Salaries
DF
3
46
49
SS
2.2344917e8
5.3790116e8
7.6135033e8
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Source
Location
Error
Total
MS
74483058
11693504
F
6.3696
p
.0011
To determine where the significant differences occurred, Tukey comparisons were made.
The salaries of accountants in the north were significantly higher than those in the central area
(p=.0059) and the north accountant salaries were significantly higher than those in the south
(p=.0013).
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Conclusions
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Geographic location does impact the salary of professional accountants. Accountants
employed in the north were found to earn significantly more than those in the central and
southern parts of the United States but were equivalent to those in the west. While the skill of
the accountant was not considered in this study, there is no reason to believe that accountants
in the north are better than those in the rest of the United States. Additional analyses with
other professional positions are recommended.
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References
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Put your APA reference list here.
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Appendices
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Put any appendices here.
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Running head: THE NUMBER OF FLIGHT DELAYS OF AMERICAN AIRLINE
The number of flight delay of American Airline
Following the American Psychological Association’s Guideline
En-Chi, Kang
Embry-Riddle Aeronautical University
1
THE NUMBER OF FLIGHT DELAYS OF AMERICAN AIRLINE
2
Table of Content
1. Abstract…………………………………………………………………………..……3
2. Introduction……………………………………………………………………………4
3. Literature Review………………………………………………………………………4
4. Research Methods……………………………………………………………………..6
5. Results…………………………………………………………………………………..9
6. Conclusions…………………………………………………………………………..10
7. Appendices……………………………………………………………………………12
8. Reference …………………………………………………………………………….15
THE NUMBER OF FLIGHT DELAYS OF AMERICAN AIRLINE
3
Abstract
As the numbers of people traveling through air transportation are growing, the
number of flights is skyrocketing as well. According to the data provided by the Bureau
of Transportation Statistics, the number of flight delays fluctuate in a certain pattern
throughout a year. As a result, it is rational to suspect that the fluctuation of flight delays
was caused by the different weather condition in different seasons. This paper explores
several published articles and statistic that reported on results from research conducted on
flight delays. The articles, however, vary in their content. Bureau of Transportation
Statistics (2019) provided the data points of every flight delays of American Airline for
the past ten years. Bai (2006) indicated that flight delays showed seasonal and weekly
patterns. Robinson (1989) indicated that the adverse weather condition had a significant
impact on the flight schedule. Zhang, et al. (2018) indicated the short-term severe
weather that covers terminal area lowers operating efficiency of the airspace. Chris
(2008) indicated that flight delays and cancellations in any given season are proportion to
the amount of the precipitation an airport receives. This paper used a One-Way ANOVA
method to determine if there were a significant difference between the number of flight
delays in different seasons for American Airline in the past ten years (2008-2017).
THE NUMBER OF FLIGHT DELAYS OF AMERICAN AIRLINE
4
RSCH 665
The number of flight delays of American Airline
Introduction
According to the Federal Aviation Administration (FAA), a flight is considered to be
delayed when it is 15 minutes later than its scheduled time. The number of flight delays
in the United States is affected by several factors, such as air carrier delay, aircraft
arriving late, security delay, weather as well as other problems. According to the data
provided by the Bureau of Transportation Statistics, the number of flight delays fluctuate
in a certain pattern throughout a year. As a result, it is reasonable to suspect that the
fluctuation of flight delays was caused by the different weather condition in different
seasons. This research paper has been planned to determine is the number of flight delays
of American Airline were different in different seasons in the past ten years (2008-2017).
Literature Review
The “Airline On-Time Statistics and Delay Causes” data published by the Bureau of
Transportation Statistics offers the data of the numbers and causes of every flight delay
from June 2003 to November 2018 for all the air carriers in the United States. It is the
main data points source for this research paper to conduct the ANOVA test.
Bai (2006) developed statistical models to analyze and assess the pattern of airport
delay, aircraft arrival delay, and schedule performance. The result of the research
indicated that the flight delay is highly related to the originate delay and it is found to
show seasonal and weekly patterns. Bai (2006) also indicated the weather condition such
as the precipitation and wind speed are contributed to flight delays, which is
THE NUMBER OF FLIGHT DELAYS OF AMERICAN AIRLINE
5
corresponded to the hypothesis of this research paper that the number of flight delays
were different in different seasons due to different weather conditions.
Robinson (1989) developed a technique to isolate the impact of weather events on
aircraft operations from all other delay-causing factors. The research determined the
difference in delay between clear days and days with inclement weather. The result of the
research showed that the adverse weather condition such as fog, thunder and snowstorms
had a significant impact on the flight schedule. Robinson (1989) also indicated that better
weather forecasts will be able to reduce delays since they would allow the ATC to more
accurately schedule the flight operations. The conclusion of the research supports the
hypothesis that the number of flight delays would fluctuate due to different weather.
Zhang, et al. (2018) indicated the short-term severe weather that covers terminal
area lowers operating efficiency of the airspace. Furthermore, different rerouting and
holding strategies implemented by pilots to avoid the weather will increase the distance
of flight path which leads to a great extension of the flight arrival time and causes flight
delays. Different seasons contain different kind of weather conditions, it is reasonable to
predict that the number of flight delays will be different in different seasons.
Chris (2008) indicated that flight delays and cancellations in any given season are
proportion to the amount of the precipitation an airport receives. Research published by
the WeatherBill showed the precipitation caused far more extensive delays and
cancellations that did temperature variations. It also indicated there is seasonal variation
in the duration of the average arrival or departure delay. The result supports the
hypothesis that there was a significant difference in the flight delay numbers in different
seasons.
THE NUMBER OF FLIGHT DELAYS OF AMERICAN AIRLINE
6
Research Methods
In order to investigate the impact of seasons on the number of the flight delays. The
“Airline On-Time Statistics and Delay Causes” data published by the Bureau of
Transportation Statistics was used (Bureau of Transportation Statistics, 2019). To be more
specific, American Airlines was selected for this study because the airlines had the largest
number of flights. This research gathered all the flight data of American Airline and
summed it up to acquire all the actual number of the flight delays in the past ten years. In
the meantime, I also divided those data into different seasons. The causal-comparative
study examined one independent variable and one dependent variable. Seasons served as
the independent variable, while the number of flight delays served as the dependent
variables. The researcher was unable to manipulate the independent variable resulting in
the study as causal comparative. Each season was classified as Spring, Summer, Fall, and
Winter based on Months. Table 1 shows the months in each season.
Table 1: Classification of Seasons
Spring
Summer
Fall
Winter
March
June
Septembe
Decembe
July
August
r
October
November
r
January
February
April
May
The number of flight delays of American Airlines in each month served as the
dependent variable. The data was not collected by the researcher but there is no reason to
question the quality of the data. A power analysis will be conducted if the statistical test
does not find any differences. The unit of analysis for this study was the month, therefore,
THE NUMBER OF FLIGHT DELAYS OF AMERICAN AIRLINE
7
increasing the number of cases will not be possible to increase insufficient power. The
mean number of flight delays for each season season are shown in Table 2.
Table 2: Mean numbers of flight delay
Season
Spring
Summer
Fall
Winter
n
30
30
30
30
Mean
10747.033
13273.600
9990.8333
10187.2330
Standard Deviation
2883.0679
4229.5020
6971.0810
2886.1976
The null hypothesis for this study was Ho: μ1 = μ2 = μ3 = μ4, which means there was
no difference in the number of flight delays in different seasons in the past ten years.
While the alternate Hypothesis was Ha: μ1 ≠ μ2 ≠ μ3 ≠ μ4, which means that there was a
significant difference in the number of flight delays in different seasons in the past ten
years. In other words, it was anticipated the number of flight delays were different in
different seasons. With the extremely large number of flights for American Airline in the
United States, it is reasonable to assume that the data will have a normal distribution.
According to the decision tree for statistical tests, this research determined to use the
ONE-WAY ANOVA test, since the paper was determined to compare the relationship
between multiple groups with one independent variable. Which means the One-Way
ANOVA is the most appropriate statistic method to conduct the research. Therefore, a
One-Way ANOVA method with a 0.05 level of significance was used to compare the
number of flight delays of the four seasons.
Results
The number of flight delays for each season was compared using a One-Way
ANOVA. The independent variable in the study was the seasons and the dependent
THE NUMBER OF FLIGHT DELAYS OF AMERICAN AIRLINE
8
variable was the number of flight delays of American Airline in the past ten years. As
shown in Table 4, significant differences were found in the number of flight delays based
on the seasons. (F=3.32, P=0.0223). These results can be interpreted that at least one
season had a significant different number of flight delays.
Table 3: ANOVA Analysis of the number of flight delays
Source
Season
DF
3
SS
2.0707157e8
MS
69023857
F-Stat
3.321373
P-value
0.0223
9
Error
Total
116
119
2.4106793e9
2.6177509e9
20781718
To determine where the significant differences occurred, Tukey comparisons were
made. The result is shown in Table 4. The number of flight delays during summer months
were significantly higher than those in the Winter (P=0.0481) and the number of flight
delays during summer months were significantly higher than those in the Fall (P=0.031).
Table 4: Tukey HSD results (95% level)Winter subtracted
from
Difference
Spring
559.8
Summer 3086.367
Fall
-196.4
Spring subtracted from
Difference
Summer 2526.567
Fall
-756.2
Summer subtracted from
Difference
Fall
-3282.77
Conclusions
Lower
-2508.38
18.19035
-3264.58
Upper
3627.976
6154.543
2871.776
P-value
0.9643
0.0481
0.9983
Lower
-541.61
-3824.38
Upper
5594.743
2311.976
P-value
0.1447
0.918
Lower
-6350.94
Upper
-214.59
P-value
0.031
THE NUMBER OF FLIGHT DELAYS OF AMERICAN AIRLINE
9
According to the result of One-Way ANOVA test, we rejected the Null Hypothesis.
Seasons do impact the number of flight delays. The number of flight delays during
Summer months were found to be significantly higher than the numbers in Winter and
Fall but were equivalent to those during Spring months. While the weather condition in
different seasons was not considered in this study, there is no reason to believe that the
weather during Summer months are worse than other seasons. Additional analyses and
researches must be done to obtain more information how and why the numbers of flight
delays during Summer were significantly greater than Winter and Fall.
THE NUMBER OF FLIGHT DELAYS OF AMERICAN AIRLINE
Appendices
Table 1: Classification of Seasons
Spring
Summer
Fall
Winter
March
June
Septembe
Decembe
April
May
July
August
r
October
November
r
January
February
Table 2: Mean numbers of flight delay
Season
n
Mean
Standard Deviation
Spring
30
10747.033
2883.0679
Summer
30
13273.600
4229.5020
Fall
30
9990.8333
6971.0810
Winter
30
10187.2330
2886.1976
Table 3: ANOVA Analysis of the number of flight delays
Source
Season
DF
3
SS
2.0707157e8
MS
69023857
Error
Total
116
119
2.4106793e9
2.6177509e9
20781718
F-Stat
3.321373
9
Table 4: Tukey HSD results (95% level)Winter subtracted
from
Difference
Spring
559.8
Summer 3086.367
Fall
-196.4
Spring subtracted from
Difference
Summer 2526.567
Fall
-756.2
Summer subtracted from
Difference
Fall
-3282.77
Lower
-2508.38
18.19035
-3264.58
Upper
3627.976
6154.543
2871.776
P-value
0.9643
0.0481
0.9983
Lower
-541.61
-3824.38
Upper
5594.743
2311.976
P-value
0.1447
0.918
Lower
-6350.94
Upper
-214.59
P-value
0.031
Raw data from the Bureau of Transportation Statistics
P-value
0.0223
10
THE NUMBER OF FLIGHT DELAYS OF AMERICAN AIRLINE
Winter
Spring
Summer
Fall
2008 Month 1
13013
16653
19620
8150
2008 Month 2
15847
13829
15092
7741
2008 Month 3
16190
16167
14241
6481
2009 Month 1
11204
9533
12649
5817
2009 Month 2
9927
10759
12416
10542
2009 Month 3
6985
9490
10192
5060
2010 Month 1
7645
10607
10479
6700
2010 Month 2
8143
7035
10018
44674
2010 Month 3
8861
11
THE NUMBER OF FLIGHT DELAYS OF AMERICAN AIRLINE
9615
8381
6114
2011 Month 1
7132
7984
9303
7181
2011 Month 2
7714
11384
9407
7110
2011 Month 3
9283
10762
9489
7229
2012 Month 1
10093
8384
7934
16157
2012 Month 2
6347
6466
9759
12651
2012 Month 3
5306
8373
10242
8127
2013 Month 1
10418
7772
12796
6613
2013 Month 2
8291
11170
11352
7643
2013 Month 3
7735
9990
12
THE NUMBER OF FLIGHT DELAYS OF AMERICAN AIRLINE
8075
7010
2014 Month 1
11650
8361
14082
7810
2014 Month 2
9381
7411
12306
10288
2014 Month 3
8607
9264
11688
8903
2015 Month 1
14417
9765
10187
10074
2015 Month 2
9529
8734
15100
10179
2015 Month 3
7925
8617
14448
11304
2016 Month 1
14652
14702
20864
11982
2016 Month 2
12444
11513
22191
10603
2016 Month 3
10999
14440
20756
13
THE NUMBER OF FLIGHT DELAYS OF AMERICAN AIRLINE
10207
2017 Month 1
12834
14113
19143
8934
2017 Month 2
13999
14819
19527
10803
2017 Month 3
9046
14699
16471
7638
14
THE NUMBER OF FLIGHT DELAYS OF AMERICAN AIRLINE
15
References
Bureau of Transportation Statistics. (2019). Airline On-Time Statistics and Delay Causes.
Retrieved from
https://www.transtats.bts.gov/OT_Delay/OT_DelayCause1.asp?pn=1
Bai, Yuqiong. (2006). ANALYSIS OF AIRCRAFT ARRIVAL DELAY AND AIRPORT ONTIME PERFORMANCE.
Retrieved from
http://etd.fcla.edu/CF/CFE0001049/Bai_Yuqiong_200605_MS.pdf
ROBINSON, P. (1989). The influence of weather on flight operations at the Atlanta
Hartsfield international airport. Weather and Forecasting, 4, 461-468.
Retrieved from
http://search.proquest.com.ezproxy.libproxy.db.erau.edu/docview/25504125?
accountid=27203
Zhang, M., Liu, K., & Xianglu, K. (2018). Base of aircraft Data–based operating cost
prediction of arrival flight delay under short-term weather. Advances in Mechanical
Engineering, 10(4)
doi:http://dx.doi.org.ezproxy.libproxy.db.erau.edu/10.1177/1687814018769231
Chris Kjelgaard (2008). Weather-related flight delays can be predicted.
Retrieved from
http://www.nbcnews.com/id/24388243/ns/travel-news/t/weather-related-flightdelays-can-be-predicted/#.XHXh2IhKjDc

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