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.
Highlights
Regarding the portability of statistical testing and the search for categorical data dependencies through correlations to the causality factors of corporate governance, the current state of the knowledge of the professional community is that experts focus on some criticism, which states “correlation does not imply causality”.In addition to this, another critique mentions the absence of a proposed solution to unambiguously verify which correlation is causal, and uncertainty in how one can determine the direction of causality of factors
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
A stability diagram is created in order to process control and for use in the visual assessment of the consistency between causality and contingency
Summary
Regarding the portability of statistical testing (parametric/nonparametric) and the search for categorical data dependencies through correlations to the causality factors of corporate governance, the current state of the knowledge of the professional community is that experts focus on some criticism, which states “correlation does not imply causality”.In addition to this, another critique mentions the absence of a proposed solution to unambiguously verify which correlation is causal, and uncertainty in how one can determine the direction of causality of factors. Regarding the portability of statistical testing (parametric/nonparametric) and the search for categorical data dependencies through correlations to the causality factors of corporate governance, the current state of the knowledge of the professional community is that experts focus on some criticism, which states “correlation does not imply causality”. The prevailing, standard approach has been formulated in terms of two opposing statistical hypotheses: one representing no difference between two populations (i.e., the null hypothesis (Ho)) and the other representing either unidirectional or bidirectional options (i.e., the alternative hypothesis (Ha)). These hypotheses primarily correspond to different models. When comparing two samples of populations, the presumption is that they are from the same primary data set, so the difference between their correct means is equal to 0
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
