Description: We'll use data made publicly available from Transport for London (TFL; source of data: https://www.kaggle.com/datasets/hmavrodiev/london-bike-sharing-dataset) to estimate the relationship between the number of bikes shared around the London network on a given day, depending on weather and other characteristics to predict the capacity needed to satisfy demand at any given point. Requires R and familiarity with non-linear regression models. Students are encouraged to work with Rmarkdown or quarto to develop their dissertation.
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Estimate the relationship between the number of bikes shared around the London network on a given day, depending on weather and other characteristics to predict the capacity needed to satisfy demand at any given point. Requires R and familiarity with non-linear regression models
StatisticsHealthEconomics/london-bikes
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Estimate the relationship between the number of bikes shared around the London network on a given day, depending on weather and other characteristics to predict the capacity needed to satisfy demand at any given point. Requires R and familiarity with non-linear regression models
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