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%.

In this study, the primary researcher set out to analyze the success of Black STEM students at a PWI. Focusing on the specific details that affect success the most, such as a differing sense of belonging, racism and race-based stressors, parental education level, and access to a parent in a STEM field.