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Stability of Dependencies of Contingent Subgroups with Merged Groups: Vaccination Case Study

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  • Tomas Macak

    (Faculty of Economics and Management, Czech University of Life Sciences Prague, Kamycka 129, 16500 Prague, Czech Republic)

Abstract

The answers to extreme phenomena both in nature and in business sectors are the constructions of the distribution of random variables with extreme values. Another area in which appropriate theoretical research is conducted regarding the influence of suppressor (third) variables in categorical data. When examining dependencies in PivotTables, we often find it necessary to merge data into larger sets (e.g., due to a greater number of theoretical frequencies lower than their critical value). A phenomenon many exist wherein the partial relation is stronger than the zero relation. For example, in such a combination, instability may occur, which indicates contingent subgroups with the merged group. The dependence of dependencies is practically manifested because the data of contingent subgroups indicate inconsistent (inverted) conclusions compared to the associated group. For this reason, this paper aimed to find the critical ratios of partial probabilities in the contingency table of subgroups of the original variables, and to determine the conditions of result consistency and contingency stability, including the proof. For practical use and for the ease of repeating the proposed procedure, the solution is based on a case study that compares the effectiveness of vaccination.

Suggested Citation

  • Tomas Macak, 2021. "Stability of Dependencies of Contingent Subgroups with Merged Groups: Vaccination Case Study," Mathematics, MDPI, vol. 9(22), pages 1-12, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2917-:d:680346
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    References listed on IDEAS

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    1. Marks R. Nester, 1996. "An Applied Statistician's Creed," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(4), pages 401-410, December.
    2. Judea Pearl, 2014. "Comment: Understanding Simpson's Paradox," The American Statistician, Taylor & Francis Journals, vol. 68(1), pages 8-13, February.
    3. Margherita Carletti & Roberto Pancrazi, 2021. "Geographic Negative Correlation of Estimated Incidence between First and Second Waves of Coronavirus Disease 2019 (COVID-19) in Italy," Mathematics, MDPI, vol. 9(2), pages 1-9, January.
    4. Y. Ma, 2015. "Simpson’s paradox in GDP and per capita GDP growths," Empirical Economics, Springer, vol. 49(4), pages 1301-1315, December.
    5. Eubank, R.L. & Hsing, Tailen, 2008. "Canonical correlation for stochastic processes," Stochastic Processes and their Applications, Elsevier, vol. 118(9), pages 1634-1661, September.
    6. Gholamreza Hesamian, 2016. "One-way ANOVA based on interval information," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(11), pages 2682-2690, August.
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