Analyzing COVID-19 by Hypothesis Tests and Linear Regression
- 10.2991/978-94-6463-102-9_5How to use a DOI?
- Two-sample t test; Linear Regression
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.
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Cite this article
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 -