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The 2022 Election in the United States: Reliability of a Linear Regression Model
Kalina, Jan ; Vidnerová, Petra ; Večeř, M.
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.
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Application Of Implicitly Weighted Regression Quantiles: Analysis Of The 2018 Czech Presidential Election
Kalina, Jan ; Vidnerová, P.
Regression quantiles can be characterized as popular tools for a complex modeling of a continuous response variable conditioning on one or more given independent variables. Because they are however vulnerable to leverage points in the regression model, an alternative approach denoted as implicitly weighted regression quantiles have been proposed. The aim of current work is to apply them to the results of the second round of the 2018 presidential election in the Czech Republic. The election results are modeled as a response of 4 demographic or economic predictors over the 77 Czech counties. The analysis represents the first application of the implicitly weighted regression quantiles to data with more than one regressor. The results reveal the implicitly weighted regression quantiles to be indeed more robust with respect to leverage points compared to standard regression quantiles. If however the model does not contain leverage points, both versions of the regression quantiles yield very similar results. Thus, the election dataset serves here as an illustration of the usefulness of the implicitly weighted regression quantiles.
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Application Of Implicitly Weighted Regression Quantiles: Analysis Of The 2018 Czech Presidential Election
Kalina, Jan ; Vidnerová, Petra
Regression quantiles can be characterized as popular tools for a complex modeling of a continuous response variable conditioning on one or more given independent variables. Because they are however vulnerable to leverage points in the regression model, an alternative approach denoted as implicitly weighted regression quantiles have been proposed. The aim of current work is to apply them to the results of the second round of the 2018 presidential election in the Czech Republic. The election results are modeled as a response of 4 demographic or economic predictors over the 77 Czech counties. The analysis represents the first application of the implicitly weighted regression quantiles to data with more than one regressor. The results reveal the implicitly weighted regression quantiles to be indeed more robust with respect to leverage points compared to standard regression quantiles. If however the model does not contain leverage points, both versions of the regression quantiles yield very similar results. Thus, the election dataset serves here as an illustration of the usefulness of the implicitly weighted regression quantiles.
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The 2020 Election In The United States: Beta Regression Versus Regression Quantiles
Kalina, Jan
The results of the presidential election in the United States in 2020 desire a detailed statistical analysis by advanced statistical tools, as they were much different from the majority of available prognoses as well as from the presented opinion polls. We perform regression modeling for explaining the election results by means of three demographic predictors for individual 50 states: weekly attendance at religious services, percentage of Afroamerican population, and population density. We compare the performance of beta regression with linear regression, while beta regression performs only slightly better in terms of predicting the response. Because the United States population is very heterogeneous and the regression models are heteroscedastic, we focus on regression quantiles in the linear regression model. Particularly, we develop an original quintile regression map, such graphical visualization allows to perform an interesting interpretation of the effect of the demographic predictors on the election outcome on the level of individual states.
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Srovnání albánského a srbského pan-nacionalismu
Volný, Vít ; Jelen, Libor (vedoucí práce) ; Lepič, Martin (oponent)
Tato práce si klade za cíl analyzovat velkosrbskou a velkoalbánskou myšlenku, najít jejich kořeny, popsat jejich historický vývoj a zjistit jejich aktuální podporu v albánském a srbském prostředí. První část práce se věnuje hlavní textům, ze kterých oba pan-nacionalismy vycházejí a jejich historickému kontextu. Následně představuje i jiné alternativy velkých srbských a albánských států a celkově popisuje, jak se pan-nacionální myšlenky měnily s ohledem na vnější zahraničně-politické příčiny. V závěru první kapitoly jsou představeny koncepty, které jsou aktuální dodnes. Druhá část práce se věnuje analýze popularity pan-nacionálních myšlenek v Srbsku, Kosovu a Albánii. Práce charakterizuje politické spektrum všech tří států s ohledem na pan-nacionální program stran. Na základě toho poté hodnotí popularitu "Velké Albánie" a "Velkého Srbska" podle výsledků voleb stran s pan-nacionálním programem. Mimo volební výsledky rovněž analyzuje atmosféru ve společnosti pomocí průzkumů veřejného mínění. Na konec je poté charakterizováno rozdělní popularity pan-nacionalismu v prostoru podle podrobných výsledků voleb ultra-nacionálních stran.
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