chi-square test

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chi-square test

 
a statistical procedure for determining significant differences between frequencies observed within the data and frequencies that were expected. There are two chi-squared tests: the chi-square test of independence, which tests whether two or more series of frequencies are independent of one another; and the chi-square test of goodness of fit, which tests whether an observed frequency distribution fits a specified theoretical model. Written also χ2-test.

chi-square test

a statistical method of assessing the significance of a difference, as when the data from two or more samples, such as the numbers of females and males attending each of two colleges, are represented by a discrete number.
Synonym(s): χ2 test
References in periodicals archive ?
Table 3 presents the percentage of White and Black student-athletes that rated listed reasons as important for use of alcohol in the past year and Chi-square statistics.
Chi-square statistics with continuity correction or Yate's correction shows that at a level of significance 0.
Chi-square statistics exhibited a significant association between the classification results ([chi square] = 433.
In summary, by analogy with the classical type chi-square statistics, we have introduced the divergence measures and propose some convenient asymptotically standard normal tests for model selection based on type divergence statistics that use estimators in a quite general class.
To test this hypothesis we calculate the Chi-square statistic and its p-value.
000 * Chi-square statistic for same-sex respondents in different locations (MSA or Non-MSA) ** Chi-square statistic for sex differences between respondents in same location (MSA or non-MSA) Table 3: Frequencies and Percentages of People Using Computers at Work for Graphics or Design Men MSA Non-MSA Government 548 33.
1] is a chi-square statistic that tests the hypothesis that the four lagged values of the sentiment measure are not jointly significant when included in the estimated consumption equation given in row 1.
Only the year effect chi-square statistics are tabulated because the year effect coefficients serve as the annual indices of abundance (Tables 11-15).
0 uses the maximum-likelihood-fitting function with robust standard errors and a mean-adjusted chi-square statistic (MLM).
Table 1 presents chi-square statistics for the significance of the correlation between ownership form and distribution system for these firms.
As the chi-square statistics in Table 4 reveal, however, this exception does not produce a statistically significant result.
The book is organized to present fundamental statistical concepts first, with later chapters exploring more advanced topics and additional statistical tests such as Distributional Hypotheses, Multinomial Chi-Square Statistics, and the Chi-Square Distribution.