chi-square test


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chi-square test

n.
A test that uses the chi-square statistic to test the fit between a theoretical frequency distribution and a frequency distribution of observed data for which each observation may fall into one of several classes.

chi-square test

n
(Statistics) statistics a test derived from the chi-square distribution to compare the goodness of fit of theoretical and observed frequency distributions or to compare nominal data derived from unmatched groups of subjects

chi′-square`

(or chi′-squared`) test`,


n.
a test that uses the quantity chi-square for testing the mathematical fit of a frequency curve to an observed frequency distribution.
[1935–40]
References in periodicals archive ?
The Chi-square test is used in the cases where the elements of the basic set of measurements have only one character X, which is a random variable, qualitative or quantitative type.
The first part indicates it is a chi-square test, and the number in parentheses represents the degrees of freedom.
To compare the outcomes between the study group and the placebo group for statistical analysis, Chi-square test, t-test and MannWhitney U test were used.
The whole model shows a Chi-square test of the hypothesis that there is no difference in survival time among the effects.
The above table illustrates the chi-square test to deal with whether is there a relationship between two variables (using and not-using satellite) with political culture.
The study conducted a chi-square test between income and expense on apparel, average expenditure on t-shirt, average expenditure on pair of jeans, where income is the independent variable and latter are the dependent variables.
The forth hypothesis of chi-square test shows that because the Sig value is more than 0.
The Pearson Chi-square test shows that there is no association between the two variables equality in provisions for skill development and gender as the value of significance is .
The chi-square test was used to evaluate the Hardy-Weinberg equilibrium (HWE) in genotypic distributions and to compare differences in general characteristics between EC patients and cancer-free controls.
To assess the correlation between variables, we performed the Pearson's chi-square test, Spearman's Rank Correlation Test, linear regression and One-Way ANOVA.
The chi-square test was used to compare the observed frequencies using the Hardy-Weinberg equilibrium prediction.