The 2022 Election in the United States: Reliability of a Linear Regression Model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00581709" target="_blank" >RIV/67985807:_____/23:00581709 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11220/23:10473340

Výsledek na webu

<a href="https://relik.vse.cz/2023/download/pdf/689-Vidnerova-Petra-paper.pdf" target="_blank" >https://relik.vse.cz/2023/download/pdf/689-Vidnerova-Petra-paper.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

The 2022 Election in the United States: Reliability of a Linear Regression Model

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

The 2022 Election in the United States: Reliability of a Linear Regression Model

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

RELIK 2023. Conference Proceedings

ISBN

978-80-245-2499-3

ISSN

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e-ISSN

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Počet stran výsledku

10

Strana od-do

123-132

Název nakladatele

Prague University of Economics and Business

Místo vydání

Prague

Místo konání akce

Praha

Datum konání akce

23. 11. 2023

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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