STATISTICS
Preliminary Observations
• The Mean of AA Fly (7.167) is lesser than TT Air (10.229). This means that the average number of hours of AA Fly is lesser than TT Air during each 24-hour period i.e. (10.229-7.167=3.062).
• The sample size of AA Fly (27) is lesser than TT Air (28). Even though AA Fly’s sample is 1 day shorter than TT Air, it does not significantly affect the output as the one day does not change much (unless there had been a market shock). However based on Central Limit Theorem, if the sample size is greater than or equal to 30 samples, the sample statistics reflect the time population parameters.
• The Median data does not show much in this case as the range (4.7 AA Fly and 6.8 TT Air) is small for both AA Fly and TT Air. This means that the mean is more applicable to be used.
• The Mode of AA Fly (7.3) is much nearer to the mean (7.167) compared to TT Air (9.2) which is much further (mean of TT Air, 10.229). This means that there is a high occurrence of AA Fly clustered around its mean.
• The Standard Deviation which compares the dispersion shows that AA Fly (1.2478) is clustered more closely compared to TT Air (1.6602).
• Since to the Standard Deviation for AA Fly is closer, the variance for AA Fly (1.557) as compared to TT Air (2.756) shows clearly the dispersion rate of both variables.
In conclusion, this could mean that TT Air has maximized their usage of aircrafts and is more efficient than AA Fly.
[Com: Good work on citing the numbers on mean and standard deviation/variance, and comparing the utilisation between AA Fly and TT Air. You may want to comment on the shape of the distributions: AA Fly is closed to bell shape while TT Air is not. Also note that mode has not much purpose for the quantitative data].
In other words, TT Air’s airline is more operationally efficient and effective in planning as compared to AA Fly although the dispersion or spread for AA Fly is clustered more closer compared to TT Air. On the
• The Mean of AA Fly (7.167) is lesser than TT Air (10.229). This means that the average number of hours of AA Fly is lesser than TT Air during each 24-hour period i.e. (10.229-7.167=3.062).
• The sample size of AA Fly (27) is lesser than TT Air (28). Even though AA Fly’s sample is 1 day shorter than TT Air, it does not significantly affect the output as the one day does not change much (unless there had been a market shock). However based on Central Limit Theorem, if the sample size is greater than or equal to 30 samples, the sample statistics reflect the time population parameters.
• The Median data does not show much in this case as the range (4.7 AA Fly and 6.8 TT Air) is small for both AA Fly and TT Air. This means that the mean is more applicable to be used.
• The Mode of AA Fly (7.3) is much nearer to the mean (7.167) compared to TT Air (9.2) which is much further (mean of TT Air, 10.229). This means that there is a high occurrence of AA Fly clustered around its mean.
• The Standard Deviation which compares the dispersion shows that AA Fly (1.2478) is clustered more closely compared to TT Air (1.6602).
• Since to the Standard Deviation for AA Fly is closer, the variance for AA Fly (1.557) as compared to TT Air (2.756) shows clearly the dispersion rate of both variables.
In conclusion, this could mean that TT Air has maximized their usage of aircrafts and is more efficient than AA Fly.
[Com: Good work on citing the numbers on mean and standard deviation/variance, and comparing the utilisation between AA Fly and TT Air. You may want to comment on the shape of the distributions: AA Fly is closed to bell shape while TT Air is not. Also note that mode has not much purpose for the quantitative data].
In other words, TT Air’s airline is more operationally efficient and effective in planning as compared to AA Fly although the dispersion or spread for AA Fly is clustered more closer compared to TT Air. On the