An Analysis of Travel Efficiency, Weather Impacts, and Crowding
This project applies statistical methods to a dataset of my bus trips. The goal was to quantify the factors affecting my journey.
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Paired t-test: Comparing trips to campus versus trips back home.
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Linear Regression: Predicting arrival time based on departure time.
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Fisher's Exact Test: Analyzing the dependency between rain and bus overcrowding.
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Pearson's Chi-Square: Testing for daily crowding patterns (The "Thursday Rush").
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Bayesian Inference: Quantifying the risk multiplier of rain on the probability of bus overcrowding.
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The_Commute_Chronicles.pdf: The final compiled report. -
The_Commute_Chronicles.Rmd: The R Markdown source code. -
bus_data.txt: The dataset (Comma-Separated Values). -
bus_data_origin.jpg: Image of how the data was collected.
To knit the .Rmd file locally, ensure you have the following installed:
- R & RStudio
- LaTeX Distribution
- Fonts: Lato and Source Code Pro
If these fonts are missing, comment out the \usepackage lines in the YAML header.
Context: Developed for the Mathematical and Statistical Foundations module at University of Stirling.