Dataset 1: International Airlines, Operated Flights and Seats to and from Australia sample file

The aim of this study was to analyze the aviation industry in Australia. We employ different kind of datasets for analysis purposes.

Dataset 1 comprises of a total of 1000 cases and has about 14 variables. This is a primary dataset and it has variables ranging from different scale measurements (Bellavance, et al., 2009). We have nominal variables as well categorical and continuous variables.

Dataset 2 also is a primary dataset that was collected among the KOI students. The survey sought to find out which of the three airport cities the students would want to fly in or out of (YangJing , 2009). The samples were randomly selected such each and every participant had an equal chance of being included in the study (Aldrich, 2015). However, the study is limited by the fact that a small sample size was employed which limits generalization (Fossey, et al., 2010). Future study should ensure we have a larger sample size (Leech & Onwuegbuzie, 2009).

Summary Statistics

The mean number of flights as can be seen in the table below is 24.53 with the median number of flights recorded being 22 while the mode is 30. The standard deviation is 19.97 while the skewness value is 2.27. The value of the skewness shows that the data is highly skewed with a positive skewness.

Table 1: Descriptive statistics for All Flights
Mean	24.53
Standard Error	0.63
Median	22.00
Mode	31.00
Standard Deviation	19.97
Sample Variance	398.94
Kurtosis	8.54
Skewness	2.27
Range	150
Minimum	1
Maximum	151
Sum	24526
Count	1000

 

Figure 1: Histogram on all air flights

In the above figure, we present the histogram for all the flights. As can be seen from the plot, the data is not normally distributed but rather skewed to the right.

For the inferential analysis, the study sought to find out whether the number of flights were significantly more than 30. The hypothesis is given as follows;

H₀: The number of flights is equal to 30

H_A: The number of flights is not equal to 30

The results of the test are presented below;

Table 2: One-Sample Statistics
	N	Mean	Std. Deviation	Std. Error Mean
All Flight	1000	24.5260	19.97359	.63162

From table 2 above, we can see that the mean number of flights is 24.53 with a standard deviation of 19.97. The error of the standard mean is 0.63.

Table 3: One-Sample Test
	Test Value = 30
t	df	Sig. (2-tailed)	Mean Difference	95% Confidence Interval of the Difference
Lower	Upper
All Flight	-8.667	999	.000	-5.47400	-6.7135	-4.2345

 
The p-value for the one-sample t-test is 0.000 (a value greater than 5% level of significance) implying that the null hypothesis is rejected. By rejecting the null hypothesis we conclude that the average number of flights is significantly equal to 30.

In this section we present the numerical summary of all flights in three Australian cities

Table 4: Numerical summary

City	Average all flight
Brisbane	23.06
Melbourne	24.12
Sydney	26.94

Dataset 2: Survey Data

 
The above results shows that the mean number of flights in Brisbane is 23.06 while that in Melbourne and Sydney are 24.12 and 26.94 respectively. The results shows that the number flights for the three cities does not significantly vary (Fotheringham & Wong, 2011). However, Sydney still stands out among the three cities in terms of the number of flights recorded.

 

Figure 2: Bar chart on all flights based on the cities (airports)

In this section we present the numerical summary of all flights in three airlines

Airline	Average all flights
Singapore Airlines	79.44
Air New Zealand	30.70
Cathay Pacific Airways	26.93

 
The above results shows that the mean number of flights in Singapore is 79.44 while that in Air New Zealand and Cathay Pacific Airways are 30.70 and 26.93 respectively. The results shows that the number flights for the three airlines significantly vary with Singapore having the most outstanding number of flights. Air New Zealand and Cathay Pacific airways are almost having the same number of flights.

Figure 3: Bar chart on all flights based on the airlines

Most students interviewed (43.3%, n = 13) said to prefer flying and out through the airport in Sydney. 30% (n = 9) of the students interviewed would however prefer Melbourne while 26.7% (n = 8) of the students said to prefer Brisbane.

 

Figure 4: Bar chart on preferred airport (city)

Conclusion

The aim of this study was to analyze the aviation industry In Australia. We performed a study to find out which of the three cities in Australia students would prefer flying in or out of. Results showed that majority of the students (43.3%, n = 13) would prefer passing through Sydney while 30% (n = 9) would prefer Melbourne and 26.7% (n = 8) would prefer Brisbane. In terms of the performance of the airports, we found out that even though Sydney was still outstanding, the airport faces stiff competition from other airports such as Brisbane and Melbourne. There was however significant difference in terms of all flights made by the different airlines. In this study we only compared three airlines, namely; Singapore airlines, Air New Zealand and Cathay Pacific Airways. There was however major difference in the average all flights for the three airlines with Singapore having highest performance. In fact, Singapore had more than twice the numbers for either Air New Zealand or Cathay Pacific Airways.

References

Aldrich, J., 2015. Fisher and Regression. Statistical Science, 20(4), p. 401–417.

Bellavance, F., Georges , D. & Martin , L., 2009. The value of a statistical life: A meta-analysis with a mixed effects regression model. Journal of Health Economics, 28(2), pp. 444-464.

Fossey, E., Harvey, C., McDermott, F. & Davidson, L., 2010. Understanding and evaluating qualitative research. Australian and New Zealand Journal of Psychiatry, 36(6), pp. 717-732.

Fotheringham, A. S. & Wong, D. W., 2011. The modifiable areal unit problem in multivariate statistical analysis. Environment and Planning, 23(7), p. 1025–1044.

Leech, N. & Onwuegbuzie, A., 2009. An array of qualitative data analysis tools: A call for data analysis triangulation. School Psychology Quarterly, 22(4), pp. 557-584.

YangJing , L., 2009. Human age estimation by metric learning for regression problems. International Conference on Computer Analysis of Images and Patterns, 6(3), p. 74–82.