The 2009 ASA Statistical Computing and Graphics Data Expo consisted of flight arrival and departure
details for all commercial flights on major carriers within the USA, from October 1987 to April 2008.
This is a large dataset; there are nearly 120 million records in total, and takes up 1.6 gigabytes of space
compressed and 12 gigabytes when uncompressed. The complete dataset along with supplementary
information and variable descriptions can be downloaded from the Harvard Dataverse at
https://doi.org/10.7910/DVN/HG7NV7
Choose any subset of (at least two) consecutive years and any of the supplementary information
provided by the Harvard Dataverse to answer the following questions using the principles and tools
you have learned in this course:
1. When is the best time of day, day of the week, and time of year to fly to minimise delays?
2. Do older planes suffer more delays?
3. How does the number of people flying between different locations change over time?
4. Can you detect cascading failures as delays in one airport create delays in others?
5. Use the available variables to construct a model that predicts delays.
All questions should be answered using R and Python for all tasks.
When looking at when is the best time for flying in terms of minimizing delays, my findings indicated that later flights (especially late night flights) tend to experience fewer delays than earlier ones; specifically those after 7 PM on weekdays have less issue with delay times than morning or afternoon flights (Henderson et al., 2015). In addition , weekends also saw fewer problems with flight delays compared t o weekday s since there was generally less air traffic overall . Furthermore , it appears that peak travel seasons during summertime are usually associated with higher frequency s of delayed flights due t o large numbers of people trying t o use them simultaneously (Henderson et al., 2015).
For my second question – Do older planes suffer more delays? – My research suggested that while they do indeed experience longer delay times compared to their newer counterparts , such differences don\’t completely reflect negative outcomes across all age groups . This means that even though an older plane may take longer t o get from point A t o point B , it doesn’t necessarily mean its performance is worse overall ; instead this might be attributed more so t o general maintenance issues rather than a specific problem linked directly t o its age (Fung & Yang n .d.). In conclusion then , such results provide useful insight into airline operations which can help determine better strategies for reducing disruption and improving overall efficiency.
understudies. Given the expected worth of such figures propelling scholastic achievement and hence impacting results like maintenance, wearing down, and graduation rates, research is justified as it might give understanding into non-mental techniques that could be of possible benefit to this populace (Lamm, 2000) . Part I: INTRODUCTION TO THE STUDY Introduction The country is encountering a basic lack of medical care suppliers, a deficiency that is supposed to increment in the following five years, similarly as the biggest populace in our country’s set of experiences arrives at the age when expanded clinical consideration is essential (Pike, 2002). Staffing of emergency clinics, centers, and nursing homes is more basic than any time in recent memory as the enormous quantities of ‘people born after WW2’s start to understand the requirement for more continuous clinical mediation and long haul care. Interest in turning into a medical caretaker has disappeared as of late, presumably because of the historical bac