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Figure 1: Age of passenger
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The table that is shown above is related to the age of passengers’ frequency, and percentage frequency table. In this regard, in 1-1, the count of passengers in terms of percentage is 16%. In 2-2, the count of passengers is present at 22% (Javornik et al. 2019). In 3-4, the count of passengers is present at 40% in terms of percentage, and in >4, the count of passengers is present at 22%. These percentages' frequencies are determined depending on age. The total in this regard, is 100%.
Table 2: Type of travel by passenger
The table that is shown above is related to the type of travel by passengers’ frequency and percentage frequency table. In this regard, in the 1-1 type, the count of passengers is present at 18% (Dahim 2021). In the 2-2 type the count of passengers is present at 58%, and in 3-4, the count of passengers is present at 23%. In this regard, the total value in terms of percentage is present at 100%.
Class of passenger | |
Row Labels | Count of Passenger |
Business | 48% |
Economy | 52% |
Grand Total | 100% |
Table 1: Class of passenger
The table that is presented above is related to the frequency and percentage frequency table. In this regard, in the business class, the count of passengers is present at 48%, and in the economy class, the count of passengers is present at 52%. In this regard, the total value is present at 100%.
Figure 1: Bar chart of age
The image that is shown above is related to the bar chart of the age of the passengers. In the 3-4 segment, the maximum number of passengers is noticed, which is valued at 40% in terms of age.
Figure 2: Pie chart of type of travel
The image shown above is related to the type of travel by passengers. In this regard, in type 2-2, the maximum number of passengers is noticed in terms of percentage, and the value of the percentage in this regard is 58%.
Figure 3: Bar chart of class of travel
The image that is shown above is related to the bar chart of the class of travel of passengers. From this image, in terms of percentage, the highest number of passengers is noticed in economy class, with a value of 52%.
Minimum | 10 | |
Maximum | 60 | |
Range | 50 | |
Quratile.INC | Quartile.Exc | |
Quartile | ||
Q1 | 25 | 25 |
Q2 | 33 | 33 |
Inter quartile range | 8 | 8 |
Mean | 32.4 | |
Median | 33 | |
Variance | 133 | |
Standard deviation | 11.6 |
Table 2: Descriptive statistics for the Satisfaction variable
In the table above the descriptive statistics of the Satisfaction variable are determined regarding passengers. In this regard, the minimum value is 10, the maximum value is 60, the range value is 50, the mean value is 32.4, the median value is 33, the variance value is 133, and the standard deviation value is 11.6, In terms of quartile, in Q1 the quartile values are present at 25 for both Quartile.INC, and Quartile.Exc. From these values of quartile, the interquartile range is determined at 8.
The formula that is used for the determination of minimum is =MIN (satisfaction levels of the passengers). The formula that is used for the determination of maximum value is =MAX (satisfaction levels of the passengers). The range formula that is used in this regard, is = (maximum-minimum). The formula that is used to determine the value of the mean is =average (satisfaction levels of the passengers) (Fregnani et al. 2021). The formula that is used for the determination of the median is =median (satisfaction levels of the passengers). The formula of variance used in this regard is =varp (satisfaction levels of the passengers). The standard deviation value is determined in this regard, with the formula =stdev (satisfaction levels of the passengers). For the determination of the quartile, the formula that is used is =Quartile.Inc (satisfaction levels of the passengers, 1), =Quartile.Exc (satisfaction levels of passengers, 1), =Quartile.INC (satisfaction levels of the passengers, 2), and =Quartile.Exc (satisfaction levels of the customers,2).
Figure 4: Box and Whisker plot for the satisfaction variable split by the class of travel
The image that is shown above is related to the Box and Whisker plot of the satisfaction of passengers split by the class of travel (Campos 2023). The values that are present in this regard, are 60 in the upper level, and 10 in the lower level. In the mid-level, the values that are present are 40, 33, and 25. In the center point, the value is present at 32.
Figure 5: Scatter diagram of the score of Satisfaction against taken time with the linear regression equation on the diagram
In the image above the scatter diagram is presented of the score of satisfaction against time. In this regard in terms of time, the linear regression equation is present at y=-0.0016x+92.021 (Gholamgharehgheshlaghi 2020). In this regard, the value of R-square is present at 9 approximately (Memika and Polat, 2023). In terms of score of satisfaction, the linear regression presence is noticed at y=-0.0169x+33.521. In this regard, the value of the R-square is present at 0.003.
Figure 6: Scatter diagram of the score of Satisfaction against taken time with the linear regression equation on the diagram with variables
In the image above of the scatter diagram in terms of the score of satisfaction against time, the replacement of the y=-0.0016x+92.021 regression equation is done with the time variable. In this regard, the value of the R-square is present at 9 approximately (Koasidis et al. 2020). The satisfaction variable is replaced with the regression equation y=-0.0169x+33521. In this regard, the R-square value is present at 0.003.
Figure 7: Correlation coefficient
In the image above, the value of the correlation coefficient is present at -0.42 among satisfaction and time variables.
At -0.42 the value of the correlation coefficient is determined among satisfaction and time variables, and this indicates that, among two variables negative relationship is present.
Figure 8: Passenger travelling
The image that is shown above is related to the passengers traveling from 2007 to 2022. In this image, in the X-axis the values of the year are present, and in the Y-axis the passenger numbers (millions) are present (Sun 2023). Throughout these years, the highest number of passengers traveled in the year 2019, with a value of 41.9 million, and the lowest number of passengers traveled in the year 2021, with a value of 11.4 million.
Product type | Number of units sold | Price per unit (£) | Total (£) |
WD40 | 3 | £ 6.49 | £ 19.47 |
WD60 | 2 | £ 6.49 | £ 12.98 |
Table 3: Total values of product type
On Each Order!
The table that is shown above is related to the product type, number of units sold, price per unit (£), and total (£). In this regard, the two product types that are chosen are WD40 and WD60. For WD40 the presence of number of units sold is present at 3 units, and for WD60 the number of units is present at 2 units (Yang 2023). At £6.49 per unit, the price per unit presence is noticed for cans of oils. The total value determined in this regard is £19.47 for an oil can with product code WD40, and the total value of £12.98 is present for the oil can with the product code WD60.
The formula that is used for the determination of the total values of product types WD40, and WD60 is total=number of units sold*price.
Figure 9: Total values of product type with price per unit (£) and number of units sold
The bar chart is demonstrated above, and in this regard, the total price is shown regarding the WD60 which is the highest value at £12.98 (Feng and Yang, 2019). The price per unit is similar for both product types at £6.49.
Figure 10: Price per unit (£) and number of units of the product types
In the image above for the product type, WD40 the number of units sold is present at 3, and for product type WD60 the number of units is present at 2 units (Zhang et al. 2019). In this regard, the price per unit (£) is present at £6.49, and this value is similar for the product type WD60.
Figure 11: WD40 product type pie chart
In the image above the number of units sold is shown only for product type WD40 with a value of 3. The price per unit present in this regard is £6.49, and the total value of this product type is £19.47.
Figure 12: WD60 product type pie chart
In the image above the pie chart of the product type WD60 is presented. In this regard, the number of units sold value is present at 2 units (de et al. 2023). The price per unit value in this regard, is present at £6.49, and the total value of this product type is present at £12.98.
This has been done depending on completeness, cleanness, and accuracy. In this regard, the data errors are removed to ensure that the data is present in an accurate form without any corruption. On the data of oil cans with product codes WD40, WD50, and WD60 the data validation is done in this regard (Tedeschi and Sciancalepore, 2019). The aspects through which the total value of the product types is determined are price per unit (£), and number of units sold (£). The number of units sold is higher for the oil can with the product code WD40.
This data validation is done, for the quality, and accuracy checking of the data source of the oil cans before processing, or importing the data. The objective constraints that are present depend on those objective constraints this data validation is done.
Figure 13: Network diagram with activities sequence and the dependencies between them
The image that is shown above is related to the network diagram with activities sequence of activities A, B, C, D, and E (Ma 2021). In this regard, the presence of duration in weeks is noticed at 4, 5, 3, 5, and 3.
Figure 14: The time that is going to be taken to complete the job by conducting the forward pass
For the completion of the job, the time that is going to be taken is 20 weeks. This is determined with the help of a forward pass (Dahim 2023). The earliest start time of activity A is present in the form of 1+3, for activity B it is present in the form of 3+2, for activity C it is present in the form of 2+1, for activity D it is present in the form of 1+4, and for activity E it is present in the form of 2+1. The earliest finish time in terms of A is 1+2, and 4+1 in activities C, and D. The earliest finish time of B and C is 1+2, in activity E.
Figure 15: Critical path and non-critical path identification showing all paths' duration with the help of backward pass
In the image above the critical path is noticed in activities A, and B. The non-critical path is noticed for activities C, D, and E. These all are done with the help of backward-pass.
References
Journals
Campos, P., 2023. Impact of airport infrastructure investment on the growth of the Angolan economy: An Auto-Regressive Distributed Lag analysis. Journal of Airline and Airport Management, 13(1), pp.12-30.
Dahim, M., 2021. Enhancing the development of sustainable modes of transportation in developing countries: Challenges and opportunities. Civil Engineering Journal, 7(12), pp.2030-2042.
Dahim, M.A., 2023. Challenges and opportunities in development sustainable transportation system in Saudi Arabia. Applied Engineering and Technology, 2(1), pp.34-45.
de Mattos, B.S., Fregnani, J.A.T.G. and Tomita, J.T., Effects of the 1978 airline deregulation act on aircraft industry measured by entropy statistics.
Feng, M. and Yang, J., 2019, May. Analysis on the Influencing Factors of the Development of Air Transport Industry in China. In 2nd Symposium on Health and Education 2019 (SOHE 2019) (pp. 434-440). Atlantis Press.
Fregnani, J.A., Mattos, B.S. and Hernandes, J.A., 2021. Airline Network-Airplane Integrated Optimization Considering Manufacturer's Program Cost. ICAS 2020.
Gholamgharehgheshlaghi, R., 2020. Airport route development and its role in developing traffic connections and services: Case Mumbai–Helsinki route.
Javornik, M., Nadoh, N. and Lange, D., 2019. Data is the new oil: How data will fuel the transportation industry—The airline industry as an example. Towards User-Centric Transport in Europe: Challenges, Solutions and Collaborations, pp.295-308.
Koasidis, K., Karamaneas, A., Nikas, A., Neofytou, H., Hermansen, E.A., Vaillancourt, K. and Doukas, H., 2020. Many miles to Paris: a sectoral innovation system analysis of the transport sector in Norway and Canada in light of the Paris Agreement. Sustainability, 12(14), p.5832.
Ma, F., 2021. Digital Twin at Urban Scale for a Master Plan in the Area of the CEBU Airport (Philippine) (Doctoral dissertation, Politecnico di Torino).
Memika, T. and Polat, T.K., 2023. Internet of Things Supported Airport Boarding System and Evaluation with Fuzzy. Intelligent Automation & Soft Computing, 35(3).
Sun, Y., 2023. Research on Airport Customer Service Quality Improvement Strategy of AY Company.
Tedeschi, P. and Sciancalepore, S., 2019, June. Edge and fog computing in critical infrastructures: Analysis, security threats, and research challenges. In 2019 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) (pp. 1-10). IEEE.
Yang, Y., 2023. Research on the development strategy of air logistics at SZ Airport based on AHP.
Zhang, W., Jiang, L., Cui, Y., Xu, Y., Wang, C., Yu, J., Streets, D.G. and Lin, B., 2019. Effects of urbanization on airport CO2 emissions: A geographically weighted approach using nighttime light data in China. Resources, Conservation and Recycling, 150, p.104454.
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