Assignment 2
Muhammad Ali
2024-05-23
Introduction
ViaRunning is an annual charity run organized at the Europa-Universität Viadrina in Frankfurt (Oder). The 2023 event took place on May 10th and marked the return to an in-person format after the pandemic. Over 200 runners participated, including students, university staff, and local residents from Frankfurt (Oder) and the neighboring city of Słubice. The participants completed a 4-kilometer course, consisting of two laps around the island of Ziegenwerder.
The event raised 1,333 euros in donations, with a minimum participation fee of three euros per runner. These funds were donated to Passo Fundo e. V., an organization supporting higher education for talented young individuals in Brazil, Kenya, and Malawi.
The winners of the 2023 ViaRunning were Paweł Kmieciak from Słubice, who recorded the fastest time among men, and Cathleen Meier from Frankfurt (Oder), who completed the course in 13:55 minutes, securing the fastest time among women.
Viadrina’s President, Prof. Dr. Eduard Mühle, gave the starting signal for the run at 6:15 PM from the terrace of the Gräfin-Dönhoff-Gebäude. This event, the 15th of its kind, was organized by students under the guidance of Prof. Dr. Georg Stadtmann.
FAQ
Question 1
Q: What is ViaRun?
A: ViaRun is an annual running event that happens every year in Frankfurt (Oder)
Question 2
Q: How can I participate?
A: You can register on our website by putting in your information in the registration section.
Question 3
Q: Is there a registration fee?
A: Yes, there is a small fee of 1 Million Euros
Question 4
Q: What are the prizes?
A: Prizes include a piece of the Berlin wall, an E-Scooter, and many more.
Pre-Processing
In general, working with data often requires extensive cleaning and preprocessing to ensure its accuracy and reliability. One such example is the time fix we implemented, which involved correcting a data anomaly where the hour and minute components were incorrectly swapped in the recorded times. This process highlights the importance of meticulous data cleaning practices. Data cleaning is a fundamental step in the data analysis pipeline, aimed at identifying and rectifying errors, inconsistencies, and anomalies within the dataset. By conducting thorough data cleaning, we can enhance the quality of our analyses, leading to more accurate and meaningful insights.
Descriptives
In 2023, our dataset comprised 178 observations. Among these, 143 individuals possessed start numbers, indicating they had pre-registered for the run. Additionally, a portion of participants joined spontaneously, contributing to the higher total observation count. However, time recordings were available for only 97 individuals. Strikingly, out of the 143 pre-registered runners, merely r pre_registered_with_times had recorded times. This discrepancy suggests potential data issues or highlights a substantial no-show rate, estimated to be approximately r round(no_show_rate, 2) %. For our analysis, we had to exclude r implausible_times more observations with implausible time records. The best running time for men was 13:07 and for women was 13:55. That left us with r final_observations observations for the analysis.
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