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- All Subjects: Logistic Regression
- All Subjects: psychology
sports, banking, and other disciplines. We use predictive analytics and modeling to
determine the impact of certain factors that increase the probability of a successful
fourth down conversion in the Power 5 conferences. The logistic regression models
predict the likelihood of going for fourth down with a 64% or more probability based on
2015-17 data obtained from ESPN’s college football API. Offense type though important
but non-measurable was incorporated as a random effect. We found that distance to go,
play type, field position, and week of the season were key leading covariates in
predictability. On average, our model performed as much as 14% better than coaches
in 2018.
We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.
Psychedelics have sparked widespread interest as they offer a potential new form of therapeutic treatment. This thesis paper explores the current and upcoming psychedelics that are being researched for their use in a therapeutic setting. The main substances discussed are lysergic acid diethylamide (LSD), methylenedioxymethamphetamine (ecstasy/molly/MDMA), and ketamine (esketamine). This paper also discusses the mechanism of action for each drug and the underlying research that has been found to support the ethical use of these substances alongside talk therapy