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.

In the U.S., the annual NCAA college basketball tournament, known as March Madness, draws in millions of people trying to predict who will win. Just one problem: no one has ever created a perfect bracket. By using a player-based rating system that updates throughout the season, a “predictive model” can be created to accurately predict teams with the best shot of winning the championship, and even show which players had the most impact on a single team in college basketball.

My project goes over creating a probability model to accurately predict the probability of a shot in the NHL becoming a goal. It explores different types of models to produce the most accurate model. The study explains which variables contribute most to whether a shot results in a goal or not and of those variables how teams can control them to have the most success.

This paper recommends amendments to the Montessori teaching system, which can in turn be adapted by individual educators or administrative school boards. The proposed tools mentioned in this paper follow the tenets of Constructivist teaching, which Montessori uses as some of its core teaching values (“Who and What is Montessori?”). Constructivist teaching argues that students learn best when they are able to apply their knowledge base to new learning experiences. The word comes from the idea that students are “constructing” their knowledge base one piece at a time, a process that starts from the ground, or base layer, and builds up from that. This construction involves physical representations of concepts, or guided experiences. Contrary to traditional, “top down” teaching, students learning through constructivist teaching get to experiment with learning concepts before a teacher explains the proper theory. These teachings try to generate excitement for the subject matter as extensions of students’ prior learning. Simulation and data visualization are powerful tools that allow students to discover the patterns present in natural processes by giving them the power to affect the environment and see the results. Implementation of the learning strategies of data visualizations and simulations should improve student performance and excitement in Earth and Space Science (ESS), while also being compliant with the Montessori teaching method.

In the basketball world, perhaps one of the most sought-after feelings is that of momentum. Basketball players, coaches, analysts, and fans alike are all too familiar with the idea that a “team has momentum” during a stretch of time, or that the team needs to do something to “generate their own momentum”. In a game that appears to be an accumulation of independent possessions, what exactly does momentum really mean? My goal was to see if there is a way to quantify momentum in an NBA game, particularly by looking at the Phoenix Suns 2021-2022 NBA season.

College athletics are a multi-billion dollar industry featuring hard-working student-athletes competing at a high level for national championships across a variety of different sports. Across the college sports landscape, coaches and players are always seeking an edge they can gain in order to obtain a competitive advantage over their opponents. While this may sound nefarious, the vast amounts of data about these games and student-athletes can be used to glean insights about the sports themselves in order to help student-athletes be more successful. Data analytics can be used to make sense of the available data by creating models and using other tools available that can predict how student-athletes and their teams will do in the future based on the data gathered from how they have performed in the past. Colleges and universities across the country compete in a vast array of sports. As a result of these differences, the sports with the largest amounts of data available will be the more popular college sports, such as football, men’s and women’s basketball, baseball and softball. Arizona State University, as a member of the Pac-12 conference, has a storied athletic tradition and decades of history in all of these sports, providing a large amount of data that can be used to analyze student-athlete success in these sports and help predict future success. However, data is available from numerous other college athletic programs that could provide a much larger sample to help predict with greater accuracy why certain teams and student-athletes are more successful than others. The explosion of analytics across the sports world has resulted in a new focus on utilizing statistical techniques to improve all aspects of different sports. Sports science has influenced medical departments, and model-building has been used to determine optimal in-game strategy and predict the outcomes of future games based on team strength. It is this latter approach that has become the focus of this paper, with football being used as a subject due to its vast popularity and massive supply of easily accessible data.

College athletics are a multi-billion dollar industry featuring hard-working student-athletes competing at a high level for national championships across a variety of different sports. Across the college sports landscape, coaches and players are always seeking an edge they can gain in order to obtain a competitive advantage over their opponents. While this may sound nefarious, the vast amounts of data about these games and student-athletes can be used to glean insights about the sports themselves in order to help student-athletes be more successful. Data analytics can be used to make sense of the available data by creating models and using other tools available that can predict how student-athletes and their teams will do in the future based on the data gathered from how they have performed in the past. Colleges and universities across the country compete in a vast array of sports. As a result of these differences, the sports with the largest amounts of data available will be the more popular college sports, such as football, men’s and women’s basketball, baseball and softball. Arizona State University, as a member of the Pac-12 conference, has a storied athletic tradition and decades of history in all of these sports, providing a large amount of data that can be used to analyze student-athlete success in these sports and help predict future success. However, data is available from numerous other college athletic programs that could provide a much larger sample to help predict with greater accuracy why certain teams and student-athletes are more successful than others. The explosion of analytics across the sports world has resulted in a new focus on utilizing statistical techniques to improve all aspects of different sports. Sports science has influenced medical departments, and model-building has been used to determine optimal in-game strategy and predict the outcomes of future games based on team strength. It is this latter approach that has become the focus of this paper, with football being used as a subject due to its vast popularity and massive supply of easily accessible data.