Pearson's chi-square test

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Pearson's chi-square test

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The objectives of this paper are to: (a) to examine the effect of Koehler and Wilson's (1986) modification of the Pearson chi-square statistic on Type I error; (b) to examine the effect of the following four factors on the Type I error rate of the Pearson chi-square test of homogeneity of distributions: (1) number of species or categories; (2) number of samples per location, time or population; (3) mean abundance of species or objects; (4) aggregation as measured by the b parameter of the Taylor power law and the VM ratio.
Given these tables, a chi-square statistic tested for a relationship between production process focus and business unit performance.
As shown in the Appendix, the hypothesized affective model fit the Finnish data for all 10 ads, The chi-square statistic was insignificant (p [greater than] 0.
Although this statistic looks like the usual chi-square statistic, we simulate its distribution under [H.
11 which is distributed as chi-square statistic with three degrees of freedom under the null hypothesis and is not significant at the 5 percent level.
14) The in-sample and out-of-sample predicted distributions for the three alternative models are compared with the actual distributions using a variant of the standard chi-square statistic.
The Chi-square statistic for the effect of the industry dummy variables is also statistically significant.
Most experimental studies on gametic disequilibrium test the independence between allelic frequencies by the chi-square statistic or equivalent statistical methods (Hedrick et al.
The observed and expected number of parasites for the 12, 7, and 8 parameter models and the chi-square statistic for each are given in Tables 5, 6, and 7.
The likelihood-ratio is interpreted analogously to the traditional chi-square statistic, but has certain multivariate advantages in applied studies such as this (17).
Heterogeneity in annual rates was tested using a chi-square statistic [6].
Near the bottom of each table the total chi-square statistic (which is the sum of all of the chi-square contributions) is shown together with its respective p-value.