Search results
Results From The WOW.Com Content Network
Bowker's test of symmetry; Categorical distribution, general model; Chi-squared test; Cochran–Armitage test for trend; Cochran–Mantel–Haenszel statistics; Correspondence analysis; Cronbach's alpha; Diagnostic odds ratio; G-test; Generalized estimating equations; Generalized linear models; Krichevsky–Trofimov estimator; Kuder ...
A chi-squared test (also chi-square or χ 2 test) is a statistical hypothesis test used in the analysis of contingency tables when the sample sizes are large. In simpler terms, this test is primarily used to examine whether two categorical variables ( two dimensions of the contingency table ) are independent in influencing the test statistic ...
Pearson's chi-squared test or Pearson's test is a statistical test applied to sets of categorical data to evaluate how likely it is that any observed difference between the sets arose by chance. It is the most widely used of many chi-squared tests (e.g., Yates , likelihood ratio , portmanteau test in time series , etc.) – statistical ...
In statistics, minimum chi-square estimation is a method of estimation of unobserved quantities based on observed data. [1]In certain chi-square tests, one rejects a null hypothesis about a population distribution if a specified test statistic is too large, when that statistic would have approximately a chi-square distribution if the null hypothesis is true.
This reduces the chi-squared value obtained and thus increases its p-value. The effect of Yates's correction is to prevent overestimation of statistical significance for small data. This formula is chiefly used when at least one cell of the table has an expected count smaller than 5. = =
where and are the same as for the chi-square test, denotes the natural logarithm, and the sum is taken over all non-empty bins. Furthermore, the total observed count should be equal to the total expected count: ∑ i O i = ∑ i E i = N {\displaystyle \sum _{i}O_{i}=\sum _{i}E_{i}=N} where N {\textstyle N} is the total number of observations.
The chi-squared distribution is used in the common chi-squared tests for goodness of fit of an observed distribution to a theoretical one, the independence of two criteria of classification of qualitative data, and in finding the confidence interval for estimating the population standard deviation of a normal distribution from a sample standard ...
where χ 2 is computed as in Pearson's chi-squared test, and N is the grand total of observations. φ varies from 0 (corresponding to no association between the variables) to 1 or −1 (complete association or complete inverse association), provided it is based on frequency data represented in 2 × 2 tables.