{'fraction_non_alphanumeric': 0.010736196319018405, 'fraction_numerical': 0.004601226993865031, 'mean_word_length': 5.046296296296297, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
GRADE PERFORMANCE IN STATISTICS : A BAYESIAN FRAMEWORK

In this paper, we investigate questions involving the impact of a multitude of covariates and their interactions on the scores of a comprehensive exit exam from 121 undergraduate students in Texas State University via a hierarchical Bayesian mixture model. The model uses a mixture of Beta distributions, inflated for the purposes of modeling the behavior of students who are no-shows for the exam. We formulate the predictive probability distribution of letter grade test scores as well as the probability that a student will belong to a particular grade cluster given a set of covariate values.