T-Test Project Example – Effect of Weather Changes on Flights in Jacksonville International Airport


The analysis will focus on the effect of weather changes on the flights in Jacksonville International Airport from the month of June in 2014 when effects of Hurricane Katrina was experienced to the end of the year. It was anticipated that delays will increase by 50% due to weather related issues.

Research Question: How will the weather change affect the flight schedule from the month of June 2014 to September 2014 during when the country experienced Hurricane Katrina?

The research anticipated findings is that the weather change that resulted to Hurricane Katrina would cause major flights delays in the three months before the condition settle down. This is because, hurricanes results to visibility problems and storms which are unfavorable conditions for flights and thus, the airport will experience a higher number of delayed flights, delayed lands and even cancelled flights as a result of this bad weather experience.


This research will focus on air flights from Jacksonville International Airport which is within the areas affected by the hurricane Katrina during the 2014 incident that was experienced for four or more months. The research will collect data on delays that took place in this airport during that period due to weather problems. This information will be used to evaluate the effect of weather change on flights schedules.


The research will focus on two variables which include month of the year and weather related flight delays that took place during the month. In this case flight is a dependent variable that depends on weather. A flight could only take place if the weather is fine and fail to take place if the weather is bad. Thus, flight is a dependant variable while weather is and months are independent variables. In this case, delayed flights will be counted in four months of the Hurricane Katrina and be compared with the delay condition after the Hurricane which will be from October to December.

Data Collection

The data for this research was obtained from transportation statistic bureau which is owned by the transportation department of the united state. The link to the site is: http://www.transtats.bts.gov/OT_Delay/OT_DelayCause1.asp?pn=1. The researcher focuses on all data regarding cases of flights delays in the Jacksonville International Airport for the whole year of 2014. After obtaining the data various causes of delays were evaluated, but the weather related delays was distinguished among the most interesting cause to analyze since its effect could vary based on the tremendous weather condition experienced in the country from mid of the year due to Hurricane Katrina. This data was extracted from all other categories. Data related to weather for the entire year were extracted but only delays from the beginning of the Hurricanes to the end of the year was found relevant. To ensure that there was no bias, the data extraction considered situation during and after Katrina.

Study Design

The research will use matched pair where the weather related flights delays condition during and after Hurricane is compared. A two sided tailed analysis will also be used to evaluate the delay distribution form the evaluated six months. The research null and alternative hypotheses are provided below:

Null Hypothesis: The weather change from the month of June 2014 will cause a huge flights delay to all carriers from to and from Jacksonville International Airport than after the Hurricanes. The chance for weather invoked flights delay during this period will be more or equal to 50%: H0: P ≥0.5

Alternative Hypothesis: The weather change from the month of June 2014 to the month of September will not have any difference with weather related flights delay to all carriers from to and from Jacksonville International Airport a month before and three months after the hurricanes. The chance that there will be extra weather invoked flights delay will be less than 50%. H1: P < 0.5.

Results descriptive statistics

The descriptive statistics for the delays during the months when Hurricanes were experienced is provided below

Delays during Hurricanes 
Standard Error26.1598
Standard Deviation150.2766
Sample Variance22583.06
Confidence Level(95.0%)53.28577
Lower Quartile36
Upper Quartile256.5


The statistical mean is 159.5758 in this case. There were instances where there was no any delay and the mode is provided as zero. The data demonstrates a high standard deviation due to an extensive range of analyzed data where the numbers of delays varied extensively bases on the weather condition of the day or the entire month.

Delay Statistical description after hurricanes:

Standard Error9.312002
Standard Deviation53.49338
Sample Variance2861.542
Confidence Level(95.0%)18.96793

Lower Quartile                    0

Upper Quartile                  89

The average delayed carriage for the entire period is documented as 49.67. The standard deviation is much higher than the mean value due to a high number of uncalled flights and a high range between a minimum value and a maximum value of the delayed flights during the duration.  There were a number of instances where flight went through for an entire month without any form of interference though this highly depended on the form of carrier. This made zero the commonly repeated number.

Results confidence Intervals

To obtain the confidence interval, the range error must be computed which will then be added and subtracted from the mean value to obtain the viable range of the statistical evaluation. The error is obtain by application of this formula Za/2 * σ/√(n).  Where Za/2 is the confidence coefficient, σ is the standard deviation, where n stands for the sample size which is 33 the two cases. The confidence level for weather delayed flights during hurricanes is 95% this when statistically computed gives an error of ± 53.29. In this regard the confidence interval is X ± 53.29. This is equivalent to 159.58± 53.29. This gives a viable interval of 106.29 to 212.87. For the delays after hurricanes with confidence level of 95%, the statistically computed error is equal to ± 18.97. In this regard the confidence interval for this variable is 49.66 ± 18.97 this is equivalent into a range of 30.69 to 68.63.

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