This investigation evaluates the most effective time series model to forecast the stock price for companies that started trading during the COVID-19 stock market crash. My research involved the analysis of five companies in the technology industry. I was able to create three different machine-learning models for each company. Each model contained various criteria to determine the efficacy of the model. The AIC and SBC are common metrics among Autoregressive, autoregressive moving averages, and cross-correlation input models. Lower AIC and SBC values indicated better-fitted models. Additionally, I conducted a white-noise test to determine stationarity. This yielded an Auto-correlation graph determining whether the data was non-stationary or stationary. This paper is supplemented by a project plan, exploratory data analysis, methodology, data, results, and challenges section. This has relevance in understanding the overall stock market trend when impacted by a global pandemic.

This project tackles a real-world example of a classroom with college students to discover what factors affect a student’s outcome in the class as well as investigate when and why a student who started well in the semester may end poorly later on. First, this project performs a statistical analysis to ensure that the total score of a student is truly based on the factors given in the dataset instead of due to random chance. Next, factors that are the most significant in affecting the outcome of scores in zyBook assignments are discovered. Thirdly, visualization of how students perform over time is displayed for the student body as a whole and students who started well at the beginning of the semester but trailed off towards the end. Lastly, the project also gives insight into the failure metrics for good starter students who unfortunately did not perform as well later in the course.

This paper analyzes the impact of the December 2022 winter storm on Southwest Airlines (SWA). The storm caused delays and cancellations for all airlines, but SWA was the only major airline that was unable to recover fully. The disruption was unique due to the higher volume of people traveling during the holiday season and the lack of good alternative transportation for stranded passengers. The paper explains SWA's point-to-point (PTP) model, which allows them to offer competitive ticket prices, and organizational factors that have helped them hold a significant market share. The paper also discusses previous failures of SWA's IT and aircraft maintenance management systems and the outdated crewing system, which were not addressed until after the storm. The paper uses AnyLogic agent based modeling to investigate why SWA was so affected and why it took them so long to recover.

I have designed a college-level course to help college-aged students build and maintain healthy friendships. Every week, students will engage in collaborative activities and learn a variety of topics related to friendship, including the benefits of friendship, barriers to friendship, and friendship maintenance mechanisms. As part of their final project, students will demonstrate their knowledge of making and maintaining healthy friendships by completing a case study in which students will be expected to apply their learnings from class to a chosen friendship and observe how the friendship changes as a result. In order to establish the need for the course I made, I first conducted a literature review on friendship, loneliness, and factors that may contribute to young adults having difficulties making friends.

Water markets are a promising method for adapting to water scarcity in the western United States, and the Colorado-Big Thompson Project (CBT) market is often held up as a prime example of their potential. While much has been written about the CBT market, the current academic literature tends to eschew structural modeling of supply and demand in favor of fitting hedonic price equations, which ignore many of the market’s unique characteristics. This paper proposes a model of supply and demand for CBT water which accounts for these unique features, including transaction supply, municipality stockpiling, and differences in behavior across different types of water users. The estimation of this model is made possible by novel administrative records data on both transfers and ownership of CBT water, the processing and features of which are described in detail. While the voluminous and messy nature of the data has prevented complete estimation of the model at this point, some preliminary results are presented along with a plan for future work.