Melanoma is one of the most severe forms of skin cancer and can be life-threatening due to metastasis if not caught early on in its development. Over the past decade, the U.S. Government added a Healthy People 2020 objective to reduce the melanoma skin cancer rate in the U.S. population. Now that the decade has come to a close, this research investigates possible large-scale risk factors that could lead to incidence of melanoma in the population using logistic regression and propensity score matching. Logistic regression results showed that Caucasians are 14.765 times more likely to get melanoma compared to non-Caucasians; however, after adjustment using propensity scoring, this value was adjusted to 11.605 times more likely for Caucasians than non-Caucasians. Cholesterol, Chronic Obstructive Pulmonary Disease, and Hypertension predictors also showed significance in the initial logistic regression. By using the results found in this experiment, the door has been opened for further analysis of larger-scale predictors and gives public health programs the initial information needed to create successful skin safety advocacy plans.

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%.

Until the Supreme Court’s landmark decision in National Collegiate Athletics Association (NCAA) vs. Alston, student-athletes were not allowed to be compensated for the millions of dollars in revenue they generate for universities. While universities cannot directly pay student-athletes, student-athletes can now make money based off their name, image, and likeness (NIL). NIL legislation has the potential (and has begun to) change college recruiting with the transfer portal and free agency landscape. Now, schools can bake NIL connections into their recruiting pitch, creating a recruiting renaissance. This research is an empirical study to determine the factors that contribute to an athlete’s NIL valuation and earnings. A hierarchical mixed-model analysis run in SAS also is used to analyze the data. The significance of this study includes providing schools and athletes with vital information pertaining to their fiscal valuation during the recruiting process. The findings can help families and student athletes to better estimate expected NIL earnings.