As Wall Street giants, retail investors, and aspiring cryptocurrency trailblazers continue to flood the cryptocurrency market, the ability to predict the volatility of cryptocurrency stocks has proven to be increasingly invaluable. In this report, we detail our methodology that applies statistical machine learning techniques to predict the direction of Bitcoin stocks. Our work also aims to build upon previous research conducted in anticipating trends within cryptocurrency using statistical machine learning methods. To predict whether the direction of Bitcoin stocks will increase or decrease on a given date, we employ Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbors (KNN), Logistic Regression Analysis, Random Forest, and Decision Trees. We also perform statistical analyses of our dataset using simple linear regression, multiple linear regression, and summary statistics, and visualize these metrics to gain a more comprehensive understanding of the variables that may contribute to deciding the direction of Bitcoin stocks on a given day. Finally, we analyze our results and discuss opportunities for future work and research.