Characteristics And Distribution Of Flights In Australia From 2003 To 2018 Analysis

Research Questions

This report aims to describe the characteristics and distribution of flights in Australia between the time period September 2003 to September 2018. Analysis is carried out on airline services data of Australia and compared to the findings of (Assaf, 2010) and (Malcolm Abbott, 2012)in order to make a recommendation on improvement of airport services to the airline. The following research questions guided the study;

1. What is the shape of the distribution of the variable All Flights?
2. Is the average number of flights coming in and flying out to Australia in a month between September 2003 and September 2018 more than 30?

• Is there association between Australian City and Airlines?

In order to answer these research questions, two datasets were used, dataset 1 and dataset 2. Dataset 1 consists of secondary data on airline services for an Australian airline. The dataset consists of both qualitative and quantitative data.

Dataset 2 consist of primary data on KOI student flying experience in and out through airports in Sydney, Melbourne and Brisbane. Dataset 2 was collected through an online survey. Both qualitative and quantitative variables were collected from the online survey. An online survey was used because it saves on time and costs. However, a challenge that was experienced is a slow response rate was experienced since respondents answered the questions at their own time of will. Another limitation was the possibility of obtaining a biased sample since there was no way of selecting the sample to participate in the study.

1. Distribution of the variable All Flights.

To determine the distribution of the dataset, we first find the median and mean of the dataset. The mean is found to be 24.92 and the median is found to be 22. Since the mean is to the right of the median, it implies that majority of the data points lie to the right of the dataset median. The skewness is 2.54. Therefore, the distribution of the variable All Flights can be said to be skewed to the right.

A frequency histogram provides a graphical representation of the distribution of the variable as shown below; The frequency histogram is plotted by following the following procedure;

1. On the Data tab, in the Analysis section, click the Data Analysis tab;
2. Select histogram.

• Specify the input range and the Bin range.

1. Select the output range.
2. Click ok.

The frequency histogram clearly depicts that the variable data are skewed to the right.

1. We investigate the research question on whether the average number of flights that came in and flew out to Australia in a month between September 2003 and September 2018 is more than 30.

To answer the research question, we set the following hypothesis;

H₀: The average monthly number of flights is less than or equal to 30.

H₁: The average monthly number of flights is more than 30.

We conduct a one sample t test using excel in order to answer the research question.

t-Test: One-Sample
	All_Flights
Mean	24.91755
Variance	460.177
Observations	93671
Hypothesized Mean	30
df	93670
t Stat	-72.5125
P(T<=t) one-tail	0
t Critical one-tail	1.64487
P(T<=t) two-tail	0
t Critical two-tail	1.959989

Test statistic 

t = -72.5125.

Rejection region

We reject H0 if t>1.65

Decision rule

Since -72.5125 is less than 1.65, we fail to reject the null hypothesis. There therefore lacks sufficient evidence at the 5% level of significance to justify the claim that the average monthly number of flights is more than 30.

Datasets Used

1. Average number of flights coming in and out of the cities of Sydney, Brisbane and Melbourne

City	Singapore	Air New Zealand	Cathy Pacific Airways
Sydney	9.33	6.71	7.53
Melbourne	8.35	7.9	5.9
Brisbane	5.67	6.36	6.74

A clustered bar graph representing the above data is;

The airline Singapore had majority of its flights going and coming from Sydney, followed by Melbourne and finally Brisbane. For the airline Air New Zealand, majority of the flights were in Melbourne, followed by Sydney and finally Brisbane. For the airline Cathy Pacific Airways, majority of the flights were from and into Sydney, followed by Brisbane and finally Melbourne.

For the city of Sydney, majority of its flights were from Singapore Airlines, followed by Cathy Pacific Airways and finally Air New Zealand. For the city of Melbourne, majority of its flights were from Singapore Airlines, followed by Air New Zealand and finally Cathy Pacific Airways. For the city of Brisbane, majority of its flights were from Cathy Pacific Airways, followed by Air New Zealand and finally Singapore Airlines.

For Sydney Airport to do better than its competitors, it should make efforts to chatter more Air New Zealand planes in order to dominate in terms of the number of the three airlines.

1. Test of association

Correlation analysis is carried out to assess whether there exists association between Australian cities and airlines. The following hypothesis is tested;

H₀: There is no association between Australian cities and airlines.

H₁: there is an association between Australian cities and airlines.

Correlation is carried out by following the following procedure.

1. First create dummy variables for Australian cities and Australian airlines.
2. On the Data tab, in the Analysis section, click the Data Analysis tab;

• Select correlation.

1. Specify the input variables, that is the dummy-transformed variables for cities and airlines. .
2. Select the output range.
3. Click ok.

On performing correlation analysis, the following results are obtained;

Correlations
	Airprt	Airlne
Airprt	Pearson Correlation	1	.125
Sig. (2-tailed)		.095
N	179	179
Airlne	Pearson Correlation	.125	1
Sig. (2-tailed)	.095
N	179	179

A p-value of 0.095 is obtained. Since 0.095 is greater than 0.05, we fail reject the null hypothesis and therefore we cannot justify that an association exists between Australian cities and airlines.

1. From the output in a, we can conclude that;

For the city of Sydney, majority of its flights were from Singapore Airlines, followed by Cathy Pacific Airways and finally Air New Zealand. For the city of Melbourne, majority of its flights were from Singapore Airlines, followed by Air New Zealand and finally Cathy Pacific Airways. For the city of Brisbane, majority of its flights were from Cathy Pacific Airways, followed by Air New Zealand and finally Singapore Airlines. The airport of Sydney can therefore be seen to perform better than the other two airports of Melbourne and Brisbane.

The average number of flights coming and getting out of Sydney airport was found to be 8.05, the average number of flights for Melbourne airport was found to be 7.87 and the average number of flights for Brisbane airport was found to be 6.33. Based on these statistics, it would be reasonable to say that KOI students would have a good experience flying in and out of Sydney airport.

Conclusion

Results of the study depict that the data of flights is not normally distributed but skewed to the right. The distribution is displayed by a frequency histogram, which clearly shows a tail to the right. The results have also shown that in major cases, the All Flights mean shall not exceed 30.

Another result found out by the study is a lack of association between Australian cities and airlines flying in and out of the airports in the Australian cities. It has also been shown that Sydney airport performs better than Melbourne and Brisbane airports in terms of the flights coming and getting out of the airport. However, for Sydney to outdo its competitors, it should make efforts to chatter more Air New Zealand planes and expand the scope of operation of the airline in order to dominate in terms of the number of flights coming and getting out of the airport.

References

Assaf, A., 2010. The cost efficiency of Australian airports post privatisation: A Bayesian methodology.   Tourism Management, 31(4), pp.6-7.

Malcolm Abbott, S. W., 2012. Total Factor Productivity and Efficiency of Australian Airports. Australian Economic Review, 35(12), pp.17.