Analyzing Airlines Services Data: Insights from Dataset Analysis and Discussion

In this article analysis we will discuss about dataset and below are the summaries point:- Section 1: Introduction provides an overview of the article, highlighting the analysis of airlines services data and the data sources used. Section 2 focuses on the analysis of a single variable in Dataset 1 obtained from the Australian Government's open data source. Section 3 discusses the analysis of two variables in Dataset 1, examining the relationships between them.

Table of Contents
Section 1: Introduction...............................................................................................................................2
Section 2: Analysis of single variable in Dataset 1.....................................................................................3
Section 3: Analysis of two variables in Dataset 1.......................................................................................5
Section 4: Collect and analysis Dataset2:...................................................................................................7
Section 5: Discussion & Conclusion............................................................................................................9
References:.................................................................................................................................................9

Section 1: Introduction
a. The article based on the analysis of airlines services data. The data for the study has been provided by
Australian Government open data source, the provided data is a subset of cases of variables of an
International Airlines, Operated Flights and seats by Australian Government.
Consider the article “Department of Infrastructure and Regional Development”,
(https://bitre.gov.au/statistics/aviation/international.aspx), the analysis shows that currently sixty-two
international airlines operated scheduled services. The traffic in international scheduled passenger
increases day by day by an average of 5.3 percent per year as well as Aircraft Movements also increases
by 4.2% as compared to this year by last year.
b. The data collection can be divided into two types, which are, Primary data, which collected directly
from the employees on the basis of designed questionnaire. And, the second is Secondary data that has
been collected from other resources. (Goodwin, 2012).
The dataset 1 has been provided by Australian Government, it is a subset of subset of cases of variables
of an International Airlines, Operated Flights and seats. Thus, dataset 1 is secondary data as it is
collected by other source.
The collected data can be further classified as qualitative or quantitative dimensions, the qualitative
data contains the values for the ordinal level and the nominal level of measurements. And, the
quantitative data contain the values of interval or ratio level measurements. (Morgan, 2013).
The dataset-1 contains data of 1000 peoples which have 14 variables that are as follows:
1. The variables, In-Out, Australian city, International city, Airlines, Route, Port Country, Port region,
Service region are categorized into nominal level of measurements. So, these variables are the
qualitative variables.
2. The variables, Stops, All flights, Max seats, Year, Month number are measured into continuous scale,
thus these are the quantitative variables.
c. The dataset-2 collected by offline survey, I asked questions to passengers about the transport
facilities, In-Out country, Airlines names, Route, Port country, number of stops, year and month. Thus, it
is directly collected from the passengers, thus dataset 2 is a primary data.
The dataset2 is collected by 35 passengers, which is greater than sample size 30. Thus, the results
obtained from the study will not be biased. The variables In-Out country, Airlines names are categorized
into nominal level of measurements. So, these variables are the qualitative variables. The variables,
Year, All flights and Month number are measured into continuous scale, thus these are the quantitative
variables.

Section 2: Analysis of single variable in Dataset 1
a. The numerical for the variable “All Flight” is shown below:
All_Flights
Mean 24.51
Standard Error 0.70
Median 20.00
Mode 31.00
Standard
Deviation 22.06
Sample
Variance 486.64
Kurtosis 9.94
Skewness 2.73
Range 154.00
Minimum 1.00
Maximum 155.00
Sum
24511.0
0
Count 1000.00
According to the above numerical summary, total 1000 flight are in or out in a month, the mean number
of flight in or out flight are 25 in a month, the maximum number of in or out flights in a month are 155,
and the total number of flights in a month are 24511.
The graphical summary is shown below:
1
11
21
31
41
51
61
71
80
90
100
110
120
130
140
150
0
20
40
60
80
100
120
140
160
180
200
Frequency
Bin
Frequency

According to the above Histogram, maximum frequency is obtained for class 30 to 40, the most of the
spikes are belongs to left side and the tail to the right, thus it is a positive skewed distribution. Hence,
most of the frequencies are obtained for 31 flights in a day in month.
b. The one sample t-test will be used to check the research question. The null hypothesis and the
alternate hypothesis for the one -tailed test are given as below:
The calculations has been done in excel, the results of the analysis is shown below:
Null Hypothesis 30
Level of Significance 0.05
Sample Size 1000
Sample Mean 24.511
Sample Standard Deviation 22.0598848
Standard Error of the Mean 0.6976
Degrees of Freedom 999
t Test Statistic -7.8685
Upper Critical Value 1.6464
p-Value 1.0000
The p-Value is larger than the level of significance (0.05). So, the null hypothesis is accepted and hence
there is sufficient evidence to conclude that the average number of flights came in and flew out to
Australia in a month between Sep-2003 to Sep-2018 is not greater than 30.
Section 3: Analysis of two variables in Dataset 1
a. The numerical summary by considering three Airports namely (Sydney, Melbourne and Brisbane) and
three Airlines namely (Singapore Airlines, Air New Zealand and Cathy Pacific Airways) corresponding to the
total number of flights in all months is shown below:
Sum of
All_Flights Column Labels
Row Labels Air New Zealand Cathay Pacific Airways Singapore Airlines Grand Total
Brisbane 347 173 374 894
Melbourne 456 407 503 1366
Sydney 953 681 395 2029
According to the above table, the total number of flights in or out in all months are 4289. Out of 4289
flights, the number of flights in or out from Airport Sydney are 2029, the number of flights in or out from
Airport Melbourne are 1366 and the number of flights in or out from Brisbane are 894. Thus, maximum

number of flights in or out from Airports Sydney by Airlines namely (Singapore Airlines, Air New Zealand
and Cathy Pacific Airways).
The graphical summary by considering three Airports namely (Sydney, Melbourne and Brisbane) and three
Airlines namely (Singapore Airlines, Air New Zealand and Cathy Pacific Airways) corresponding to the total
number of flights in all months is shown below:
B r i s b a n e M e l b o u r n e S y d n e y
8.09%
10.63%
22.22%
4.03%
9.49%
15.88%
8.72%
11.73%
9.21%
Air New Zealand
Cathay Pacific Airways
Singapore Airlines
According to the above graph, maximum number of flights in or out from Airports Sydney by Airlines
namely (Singapore Airlines, Air New Zealand and Cathy Pacific Airways). The Airline Air New Zealand and
Singapore Airlines have maximum number of flights from Sydney as compared to the Airports Melbourne
and Brisbane while Cathy Pacific Airline haven’t any flight from Sydney.
b. The Chi-Square test will be used to test whether there is an association between Australian City and
Airlines.
The null hypothesis is: There is no relationship between Australian City and Airlines.
And, the alternative hypothesis is: There is a relationship between Australian City and Airlines.

The calculations has been done in STATKEY, the summary results for Chi-Square test for association by
using STATKEY is shown below:
Air New
Zealand
Cathay Pacific
Airways
Singapore
Airlines Total
Brisbane
347
366
0.988
173
262.8
30.71
374
265.1
44.699
894
Melbourn
e
456
559.3
19.068
407
401.6
0.072
503
405.1
23.65
1366
Sydney
953
830.7
18.002
681
596.5
11.957
395
601.7
71.033
2029
Total 1756 1261 1272 4289
Observed, Expected, Contribution to χ2
The Chi-Square test results are given below:
n = 4289, χ2 = 220.179
Air New
Zealand
Cathay Pacific
Airways
Singapore
Airlines Total
Brisbane 347 173 374 894
Melbourne 456 407 503 1366
Sydney 953 681 395 2029
Total 1756 1261 1272 4289
The value of the Chi-square test statistic is 220.179 and the corresponding P-Value is 0.00. So the P-
Value is less than the level of significance 0.05. Thus, the null hypothesis of the test gets rejected. Hence,
there is a relationship exist between the Australian City and Airlines.
c. According to the results obtained in part (a) and Part (b), the maximum number of flights in or out
from Airports Sydney in comparison to Airport Melbourne and Brisbane. Thus, Sydney Airport performs
best.

Section 4: Collect and analysis Dataset2:
The dataset2 is collected by 35 passengers at three Airports (Sydney, Melbourne and Brisbane) and three
Airlines (Singapore Airlines, Air New Zealand and Cathy Pacific Airways) corresponding to the total number
of flights in all months. The variables In-Out country, Airlines names are categorized into nominal level of
measurements. So, these variables are the qualitative variables. The variables, Year, All flights and
Month number are measured into continuous scale, thus these are the quantitative variables.
The numerical summary corresponding to the total number of flights in all months is shown below:
Sum of All_Flights Airline
Australian_City Air New Zealand Cathay Pacific Airways Singapore Airlines Grand Total
Brisbane 128 128 82 338
Melbourne 98 62 248 408
Sydney 508 18 199 725
Grand Total 734 208 529 1471
According to the above table, the total number of flights in or out in all months are 1471. Out of 1471
flights, the number of flights in or out from Airport Sydney are 725, the number of flights in or out from
Airport Melbourne are 408 and the number of flights in or out from Brisbane are 338. Thus, maximum
number of flights in or out from Airports Sydney by Airlines namely (Singapore Airlines, Air New Zealand
and Cathy Pacific Airways).
The graphical summary is shown below:
Brisbane Melbourne Sydney
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
8.70%
6.66%
34.53%
8.70%
4.21%
1.22%
5.57%
16.86%
13.53%
Percentage of All flights in all months
Air New Zealand
Cathay Pacific Airways
Singapore Airlines

According to the above graph, maximum number of flights in or out from Airports Sydney by Airlines
namely (Singapore Airlines, Air New Zealand and Cathy Pacific Airways). The Airline Air New Zealand and
Singapore Airlines have maximum number of flights from Sydney as compared to the Airports Melbourne
and Brisbane while Cathy Pacific Airline haven’t any flight from Sydney.
Section 5: Discussion & Conclusion
a. Executive summary:
The dataset-1 contains data of 1000 peoples which have 14 variables, the dataset-2 collected by offline
survey which contains 35 variables.
The numerical for the variable “All Flight” in dataset1 indicates the the mean number of flight in or out
flight are 24 in a month, the maximum number of in or out flights in a month are 151, and the total
number of flights in a month are 24446 and most of the frequencies are obtained for 31 flights in a day
in month. The average number of flights came in and flew out to Australia in a month between Sep-2003 to
Sep-2018 is not greater than 30.
The numerical summary by considering three Airports namely (Sydney, Melbourne and Brisbane) and
three Airlines namely (Singapore Airlines, Air New Zealand and Cathy Pacific Airways) corresponding to the
total number of flights in all months indicates that, Out of 3313 flights, the number of flights in or out from
Airport Sydney are 1495, the number of flights in or out from Airport Melbourne are 1203 and the
number of flights in or out from Brisbane are 615. The Airline Air New Zealand and Singapore Airlines have
maximum number of flights from Sydney as compared to the Airports Melbourne and Brisbane while Cathy
Pacific Airline haven’t any flight from Sydney.
The Chi-Square test indicates that, there is a relationship exist between the Australian City and Airlines.
The dataset 2 indicates that, maximum number of flights in or out from Airports Sydney by Airlines
namely (Singapore Airlines, Air New Zealand and Cathy Pacific Airways).
b. Suggestion: The analysis should be done for all the Airports and Airlines in Australia so that we can
understand the busiest airport and the busiest Airlines.
References:
Goodwin, S. (2012) SAGE secondary data analysis. India: SAGE publications Pvt. Ltd.
Morgan, D. (2013) Integrating Qualitative and Quantitative methods: A Pragmatic Approach. India:
SAGE publications Pvt. Ltd.