Pearson's chi-square test

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Pearson's chi-square test

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Our purpose in this paper is to recommend a new selection methodology based on the P-Value associated with the well-known Pearson's chi-squared test ([chi square]) when testing for dependence between state of nature (disease present or not present) and evidence (test positive or negative measures).
Alternatively, as we propose in this paper and show below by way of hypothetical example, the cutoff point that establishes the Criterion Standard Test is the one that corresponds to the lowest P-Value of the Pearson's chi-squared test ([chi square]).
To complete this paper there have been used methods of analysis, comparison, induction and deduction, percentage of positive and negative answers and Pearson's chi-squared test.
The proportional representation of positive and negative answers and the Pearson's chi-squared test were used for evaluation of research.
All the data were in categorical format therefore using descriptive statistics frequency and percentages were measured for type of domestic accidents, pattern of domestic accidents and Pearson's chi-squared test (2) was used to see association between these variables.
Ttest was used for comparing mean in two different groups and Pearson's chi-squared test was used to find association between respiratory disease and vitamin D.
The difference in the number of days sampled does not interfere in the Pearson's Chi-squared Test for Count Data as this test evaluates proportions and not absolute values of ephippia collected.
Pearson's chi-squared test was used to examine the associations between socio-demographic data, SE factors, PA, weight status, and DQ.
The analysis is fundamentally, a study of two variables with the application of statistical decision tests: Pearson's Chi-squared test, Phi coefficient, Pearson's coefficient correlation, Goodman's Lambda coefficient and the factorial analysis of variance (ANOVA).
The significance level of Pearson's chi-squared test was 0.
The data were processed by using the statistical program IBM SPSS Statistics 22, which was subsequently analyzed the dependency between the two nominal variables by means of contingency tables and Pearson's chi-squared test.
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