This is the code base for a final project completed for Harvard's Linear Models course, STAT 139. The project team consisted of Nicholas Stern, Phoebe Wong, Yiming Xu, and Zizheng Xu. The goal of the project was to carry out a fleet of statistical tests to explore the mechanisms that drive Amazon product ratings.
The dataset we used was sourced with the permission of Professor Julian McAuly from the University of California San Diego. A publicly available sample of the datasets can be found here. This dataset contains product reviews and metadata from Amazon. In total the data accounts for 142.8 million reviews spanning May 1996 to July 2014.
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We loaded and cleaned the data in Python, using the jupyter notebook labeled amazon_load.ipynb.
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The code corresponding to teh inter-categorical tests is located in intercat_tests.Rmd
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We built the linear model in lm.Rmd