Original title: The 2022 Election in the United States: Reliability of a Linear Regression Model
Authors: Kalina, Jan ; Vidnerová, Petra ; Večeř, M.
Document type: Papers
Conference/Event: RELIK 2023: Reproduction of Human Capital - mutual links and connections /16./, Praha (CZ), 20231123
Year: 2023
Language: eng
Abstract: In this paper, the 2022 United States election to the House of Representatives is analyzed by means of a linear regression model. After the election process is explained, the popular vote is modeled as a response of 8 predictors (demographic characteristics) on the state-wide level. The main focus is paid to verifying the reliability of two obtained regression models, namely the full model with all predictors and the most relevant submodel found by hypothesis testing (with 4 relevant predictors). Individual topics related to assessing reliability that are used in this study include confidence intervals for predictions, multicollinearity, and also outlier detection. While the predictions in the submodel that includes only relevant predictors are very similar to those in the full model, it turns out that the submodel has better reliability properties compared to the full model, especially in terms of narrower confidence intervals for the values of the popular vote.
Keywords: elections results; electoral demography; linear regression; reliability; variability
Project no.: GA21-19311S (CEP)
Funding provider: GA ČR
Host item entry: RELIK 2023. Conference Proceedings, ISBN 978-80-245-2499-3

Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: https://relik.vse.cz/2023/download/pdf/689-Vidnerova-Petra-paper.pdf
Original record: https://hdl.handle.net/11104/0349891

Permalink: http://www.nusl.cz/ntk/nusl-538555


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Research > Institutes ASCR > Institute of Computer Science
Conference materials > Papers