Historical US Oil and Weather
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Nicole Yeh
CoPIs:
Liam Healy, Christopher Lecoq
College:
The Dorothy and George Hennings College of Science, Mathematics, and Technology
Major:
Computer Information Systems
Faculty Research Advisor(s):
Ching-yu Huang
Abstract:
This project surmises that there is a correlation between severe weather events and the
economic success of the areas they occur. Using three datasets, one focused on weather and two
on economic data we plan to compare the intensity and number of weather events in an area to its
prosperity economically. The first dataset consists of major weather events throughout the United
States from January 1996 to November 2023. Forty-eight different weather event types are
included such as, tornadoes, hurricanes, and winter storms. Key attributes included in this dataset
include the location, given by latitude and longitude, county, and state, the type of event,
damages, injuries and deaths, and duration of event. The second dataset is stocks of XOM
(Exxon Mobil Corporation) opening and closing stock prices from January 1996 through
November 2023 collected from Yahoo!Finance. The third dataset is the average retail gasoline
price in the United States for all formulations, reported weekly from April 1993 to February
2024. We plan to take the common dates between these sets, January 1996 to November 2023
and look for a correlation between the number of weather events occurring in the United States
and the price of gasoline. We will also compare the rising and falling of gas prices in the United
States to the stock price of gasoline company Exxonmobil, to see if their stock suffers or
prospers when prices are low or high respectively.