Date of Award

2023

Document Type

Thesis

Degree Name

Bachelors

Department

Natural Sciences

First Advisor

Perez, Tiago

Area of Concentration

Data Science

Abstract

This thesis will be focusing on the Covid-19 Pandemic while looking at some of the tertiary industries such as the streaming service Netflix and the gaming Company Steam. Throughout this paper, the methods used are linear/multiple linear regression, k means clustering, and total lag cross-correlation. This is important since most research papers mention primary industries like the housing market, and the hospitality industries, and negative impacts like the number of deaths and the businesses that had filed for bankruptcy. The main method of this thesis revolves around the use of linear/multiple linear regression of time series-based data. In doing so, other models and different types of plots such as clustering and total lag correlation plots are also formulated. The most significant result is Netflix shows a small but noticeable jump in total revenue earned over the course of the Pandemic, but Steam has a much larger and noticeable jump in popularity over the course of the Pandemic. The results show that there is no correlation between the Covid-19 pandemic and these industries, but the plots of linear regression and cluster analysis prove otherwise.

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