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Full-Text Articles in Longitudinal Data Analysis and Time Series
Modeling Housing Prices: Which Features Matter Most?, Alex Ruvolo
Modeling Housing Prices: Which Features Matter Most?, Alex Ruvolo
Williams Honors College, Honors Research Projects
This paper attempts to find the biggest factors and traits that influence the cost of housing. This will include the lot size, type of street, utilities, neighborhood, year built, heating, electrical, yard size, number of different rooms, age, condition, and others. I will attempt to answer the question of whether the prices of houses have changed within the last 5 to 10 years, and obviously this is an easy question to answer. However, the bigger question beyond this is are the main factors affecting housing prices all important in explaining this relationship? Is one factor more important than the rest …
Time Series Modeling Of Akron Air Quality Index (Aqi) Data, Mason Yurich
Time Series Modeling Of Akron Air Quality Index (Aqi) Data, Mason Yurich
Williams Honors College, Honors Research Projects
With the increase in population and industrialization around the world, climate has become a major concern for many researchers. One measure that has drawn much interest is air quality. There are available resources that track the Air Quality Index (AQI) in most large cities, but there is a general lack of information regarding Air Quality forecasts, even for one day in the future. This project aims to find a useful statistical model for representing and predicting the AQI measure in Akron, Ohio over time. By using historical air quality data from the United States Environmental Protection Agency and AQI.in, an …
Impacts Of Covid-19 On Industrial Growth In The United States, Emily G. Warthman, Charles J. Landis
Impacts Of Covid-19 On Industrial Growth In The United States, Emily G. Warthman, Charles J. Landis
Williams Honors College, Honors Research Projects
COVID-19 has caused massive ramifications on all parts of life in the world and industry growth/decline is not immune to it. This report will analyze nine different industries’ profit and revenue from quarterly data during the years 2009-2022. Forecast models will be generated using various methods and different techniques of validating to predict the values from Q2 2020- Q4 2022 based on historical data. After which, a comparison will be conducted between those predicted values to the actual average revenue and profit generated by order of greatest error percentage made. Thorough research will then be completed to determine if there …
Home Sales As A Time Series Model, Noah R. Hellenthal
Home Sales As A Time Series Model, Noah R. Hellenthal
Williams Honors College, Honors Research Projects
Rational Expectations Hypothesis is an economic theorem that states that our best way to predict the future is by looking at the past. While this theory is typically used to address inflation, the same concept can be used when predicting future home sales. With the failure of subprime mortgages and the burst of the housing market bubble in 2008, home sales are proven to be an appropriate indication of how the U.S. economy is performing. Through time series analysis, I will be able to construct a model with monthly home sales data from the U.S. Census Bureau. Due to seasonality …
Global Warming Statistical Analysis, Jared Skinner
Global Warming Statistical Analysis, Jared Skinner
Williams Honors College, Honors Research Projects
This paper will investigate global warming and its effects on natural disasters. I will review the historic movements of climate change and activism, as well as the current discussions surrounding global warming. Secondly, I will examine various datasets, paying attention to the severity and frequency of specific natural disasters. I will then touch briefly on the topic of catastrophe modeling as it relates to the increased risk and losses associated with the discussed natural disasters and how those put the problem of global warming in a framework which financial and government institutions can grasp. I will also be analyzing economic …
Investigating Use Of Beta Coefficients For Stock Predictions, Jeffrey Swensen
Investigating Use Of Beta Coefficients For Stock Predictions, Jeffrey Swensen
Williams Honors College, Honors Research Projects
By using previous stock market data, investors can get a good sense of how to invest for the future. A common way to determine what stocks are riskier than others is by using the beta coefficient. This paper investigates the relationship between the overall S&P 500 market and certain individual stocks to see if we can use past stock return data to predict the future riskiness of certain stocks. Correlation between the individual stocks and the S&P 500 will allow us to determine the relationship between the two. Finding the beta coefficients for the individual stock market will allow investors …