Data and code for Uncertainty Displays Using Quantile Dotplots or CDFs Improve Transit Decision-Making (CHI 2017)
This repository contains data and analysis code for the following paper:
Michael Fernandes, Logan Walls, Sean Munson, Jessica Hullman, and Matthew Kay. "Uncertainty Displays Using Quantile Dotplots or CDFs Improve Transit Decision-Making", Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI 2018. DOI: 10.1145/3173574.3173718
Additional materials from the final analysis are available here:
data/final_trials.csv: data from the final study
models/final_model.rds: fitted model object from final Beta regression. This is a
experiment_screen_shots.pdf: Screenshots of the major end points encountered by subjects in our online experiment. Includes an example tutorial that walks a subject through the important details of how to use one of the uncertaintiy visualizations (probability density plots).
Some (rougher) pilot analysis notebooks are also included in this repository: