Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022)

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Analyzing COVID-19 by Hypothesis Tests and Linear Regression

Authors
Yi Lu1, Yifan Yang2, *
1Stony Brook University, College of Engineering & Applied Science, Brookhaven, NY, United States
2University of Wisconsin-Madison, College of Letters & Science, Madison, WI, United States
*Corresponding author. Email: yang677@wisc.edu
Corresponding Author
Yifan Yang
Available Online 29 December 2022.
DOI
10.2991/978-94-6463-102-9_5How to use a DOI?
Keywords
Two-sample t test; Linear Regression
Abstract

The outbreak of COVID-19 has caused urgent global challenges due to its rapid contagious characteristics. Analyzing known data from the past is one way to effectively control the spread of the pandemic. The United States is a racially diverse country; therefore, the composition of social groups is relatively complex. This article selects the confirmed cases data from March to June 2020 in Chicago for analysis. The data was divided into three categories: age, gender, and race. Latinos and blacks are more worthy of attention in the racial category, and young and middle-aged in the age group are more significant. This paper analyzes the existing data set through basic data processing, two-sample t-test and linear regression. We propose a regression model with dummy variables to analyze the generic covid data. There was not much difference between men and women in the number and rate of diagnoses, so the effect of gender in subsequent tests was not considered. In terms of age, the number and rate of confirmed diagnoses are higher in the 18 to 49-year-old group; the Latino group is more prominent among different ethnic groups, followed by blacks and whites. Finally, we put forward targeted epidemic prevention suggestions for different groups of communities and companies.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022)
Series
Atlantis Highlights in Computer Sciences
Publication Date
29 December 2022
ISBN
978-94-6463-102-9
ISSN
2589-4900
DOI
10.2991/978-94-6463-102-9_5How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yi Lu
AU  - Yifan Yang
PY  - 2022
DA  - 2022/12/29
TI  - Analyzing COVID-19 by Hypothesis Tests and Linear Regression
BT  - Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022)
PB  - Atlantis Press
SP  - 29
EP  - 37
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-102-9_5
DO  - 10.2991/978-94-6463-102-9_5
ID  - Lu2022
ER  -