The airline industry is experiencing massive competition (Belobaba, Odoni, & Barnhart, 2015). Since the beginning of the 21st century, the industry has experienced massive expansions and it is still expanding both domestically and globally (Clausen et al., 2010). As a result, the industry has facilitated growth for world trade, economies and international investment (Morrison & Winston, 2010). Consequently, it has become a major component for the tourism and leisure industry (Morrison & Winston, 2010). Airline services data of Australia was presented to develop recommendations which can be used to improve the Airport Services to airlines in Australia. Various statistical tests were carried out and the output presented.
Dataset 1 is a dataset assigned specifically for this research. The data is secondary in nature since it was available from the internet as it was provided by the Australian Government Open Data. The dataset entails thirteen variables. The variables and their characteristics are as shown in the table below:
Dataset 2 description
For dataset 2, a questionnaire was developed to capture the desired data. The questionnaire entailed of only five questions.
From the table above, it can be seen that all the variables were categorical. Once developed, the questionnaire was administered to KOI students. Since the data was captured directly from the respondents, then it was primary in nature (Saris & Gallhofer, 2014). The advantage of carrying out surveys directly is that the instances of bias is minimal (Rowley, 2014). Thus, the sample was not biased. The method of simple random sampling was considered in obtaining a sample of 30 KO students. However, the limitations of using the data is that the 30 participant used in the survey may not be representative of the whole population.
Section 2
All flights shape of the distribution
The table below summarises the data for all flights.
From the table 1, it is evident that most of the data lie within the frequency of 1-20 and 20-40. As the classes progress the count of all flights decreases. From this, it can be expected that the distribution will be positively skewed (Forbes et al., 2011).
Figure 1 above confirms the assumption based on table 1 that the distribution is positively skewed. It is seen that the all flights distribution is skewness tends to the right hence it is positively skewed (Wolfe, Palmer, & Horowitz, 2010). Consequently, it can be deduced that the median of the data is greater than the mean.
Hypothesis testing
The following hypothesis was developed to determine if the number of flights on average between September 2003 and September 2018 are more than 30.
H0: The flights total on average is more than 30.
H1: The flights total on average is less than 30.
The selected level of significance is 0.05.
Thus;
The mean of the all flights data is 26.5 while the standard deviation is 22.2.
Hence,
Z = (x? – μ) / (σ/√n)
= (26.5 – 30) / (22.2/√1000)
= -4.99
The p-value of -4.99 z statistics is:
P (z < -4.99) = 0.00
We choose to not accept the null hypothesis since the p-value < 0.05. Thus, it can be resolved that between Septembers 2003 and September 2018, the average number of flights that came in and flew out to Australia in a month is less than 30.
The following table summarises which airport (Sydney, Brisbane & Melbourne) performs best among the three with regards to three Airlines which include Singapore Airlines, Air New Zealand and Cathy Pacific Airways.
Table 3 shows that Sydney Airport is the busiest with 1,815 total flights compared to Melbourne (with 1,084 total flights) and Brisbane which was ranked least with major 1,084 total flight. In the three cities, Air New Zealand had the most flights with a total of 1,794 while Singapore Airlines had the least with a total of 1,169.
However, to determine how the airlines compete in the three cities match, the following figure was used.
For Air New Zealand, it is apparent that Sydney airport was the busiest with 696 total flights compared to Melbourne which had 582 flights and Brisbane which had 516 total flights. A similar observation was experienced for Cathay Pacific Airways where Sydney had the highest number of total flights (639) while Melbourne and Brisbane had 432 and 123 total flights respectively. Though Sydney had the highest number of total flights for Singapore Airlines Brisbane city peeped Melbourne to be ranked second with 445 total flights while Melbourne was ranked las with 244 total flights.
A suitable test to perform a 0.05 level of significance to determine if there is an association between the Australian Cities (Sydney, Melbourne & Brisbane) and the three Airlines (Cathay Pacific Airways, Air New Zealand, and Singapore Airlines) is a chi-square test. The chi-square test was adopted since it is used in discovering of there is a relationship between two or more variables (McHugh, 2013). The assumption that was made was that the variables under consideration were categorical.
The developed hypothesis is as follows:
H0: The three Australian Cities and the three Airlines have no association
H1: The three Australian Cities and the three Airlines have an association
It was found out that there is a statistically significant difference since the p-value is less than 0.05 (Greenland et al., 2016). Hence we choose to not accept the null hypothesis. Therefore, it can be determined that there is a relationship between the three Australian Airlines and the three Cities.
Conclusion
From part b output it was seen that there was an association between the three Australian Cites and the three airlines. Hence, the three airiness do not equally prefer the Airport to use but favour one or two of the airports. As a result, it was observed that the Sydney Airport performed best among the three airports since it was the most preferred by numbers by the three airlines.
To find whether KOI students had a good experience in flying both in and out via the Sydney, Melbourne and Brisbane Airports, a survey was ran to acquire the data. The figure below show the satisfaction levels of the students who used the three airports.
From the survey, it was seen that Sydney Airport was the most fulfilling since majority of the participants were satisfied with it. However, it surpassed the other airports in terms of being unsatisfied, not sure and moderately satisfied; a factor that can be attributed to the many participants who went through it. Melbourne came second since most of its users were very satisfied with the services while only one person was unsatisfied. Brisbane performed poorly as majority of its users were not sure while very satisfied users were only 4.
Discussion & Conclusion
Evidently, the Arline industry is a very competitive industry (Bieger & Agosti, 2017). The number of flights within the various airports has been seen to be between 1 and 40. However, on further scrutiny, it was found out that between September 2003 and September 2018, the average number of flights which came in and flew out to Australia in a month was less than 30.
Sydney airport is the busiest airport compared to Melbourne and Brisbane. It massive traffic can be majorly be attributed to Singapore Airlines, Air New Zealand, and Cathay Pacific Airways. Since there was no link between the three Australian cities and the three Airlines, there can be attributed to be other factors which has made Sydney to top the ranks (Berry & Jia, 2010). A survey carried out found out that majority of the participants who used the three airports were mainly very satisfied with Sydney airport while Brisbane airport had mixed results. Thus, Sydney Airport needs to improve its services even further by coming up with innovations that will ensure the airport remains at the top of the pecking order (Erdil & Yildiz, 2011).
The statistical analysis findings can be enriched further by carrying further research in the future. Since the research was carried out only for Australia, other countries globally can be incorporated to improve it and make it more representative of Airports internationally.
Reference
Belobaba, P., Odoni, A. and Barnhart, C. eds., 2015. The global airline industry. John Wiley & Sons.
Berry, S. and Jia, P., 2010. Tracing the woes: An empirical analysis of the airline industry. American Economic Journal: Microeconomics, 2(3), pp.1-43.
Bieger, T. and Agosti, S., 2017. Business models in the airline sector–evolution and perspectives. In Strategic management in the aviation industry (pp. 41-64). Routledge.
Clausen, J., Larsen, A., Larsen, J. and Rezanova, N.J., 2010. Disruption management in the airline industry—Concepts, models and methods. Computers & Operations Research, 37(5), pp.809-821.
Erdil, S.T. and Y?ld?z, O., 2011. Measuring service quality and a comparative analysis in the passenger carriage of airline industry. Procedia-Social and Behavioral Sciences, 24, pp.1232-1242.
Forbes, C., Evans, M., Hastings, N. and Peacock, B., 2011. Statistical distributions. John Wiley & Sons.
Greenland, S., Senn, S.J., Rothman, K.J., Carlin, J.B., Poole, C., Goodman, S.N. and Altman, D.G., 2016. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European journal of epidemiology, 31(4), pp.337-350.
McHugh, M.L., 2013. The chi-square test of independence. Biochemia medica: Biochemia medica, 23(2), pp.143-149.
Morrison, S. and Winston, C., 2010. The evolution of the airline industry. Brookings Institution Press.
Rowley, J., 2014. Designing and using research questionnaires. Management Research Review, 37(3), pp.308-330.
Saris, W.E. and Gallhofer, I.N., 2014. Design, evaluation, and analysis of questionnaires for survey research. John Wiley & Sons.
Wolfe, J.M., Palmer, E.M. and Horowitz, T.S., 2010. Reaction time distributions constrain models of visual search. Vision research, 50(14), pp.1304-1311.
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