COVID-19 Incidence Rate in Minnesota: A Bayesian Spatial Analysis
Individual author or multiple authors
Group
Major
Statistics (STAT)
Category of Work
Comps
Additional Category of Work
None
Degree
Bachelor of Arts
Class Year
2022
Comps Adviser(s)
Bastola, Deepak
Special Recognition
Nathan Hayes-Rich - Distinction on comps
Identifier (Includes All Files and Enter All Their Files Name)
pollardc_2022_STAT_paper.pdf
Student Project URL If Available
https://bayesian-spatial-modeling-covid- mn.shinyapps.io/covid app/
Keywords
Bayesian, Covid, spatial, model, Minnesota
Academic Civic Engagement (ACE) Comps Designation
no
Format
application/pdf
Files Uploaded
Text (paper)
Rights Management
Student author/s retain copyright to this work. Through online submission process, author/s granted Carleton College the non-exclusive rights to preserve this work as part of Carleton's academic history and to use it for teaching purposes and/or institutional research and assessment.
