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.
Miller-Keane Encyclopedia and Dictionary of Medicine, Nursing, and Allied Health, Seventh Edition. © 2003 by Saunders, an imprint of Elsevier, Inc. All rights reserved.

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
Farlex Partner Medical Dictionary © Farlex 2012
References in periodicals archive ?
According to the Pearson chi2 test, there is a statistically significant association between the patients' pathologies and the presence of paresthesia.
There was no significant differences in statistics: Pearson Chi2 test = 3.4156, p = 0.065.
The Chi2 test of average difference between members and non-members of loyalty programs are significant for two variables with a risk of 5%.
Four of the variables show different behavior from the other sectors (Chi2 test).
The results of Chi2 test demonstrated that the presence of galactorrhea and irregular menstruation were also significantly lower in patients of group B (Cabergoline) than group A (Bromocriptine) (P<0.001 and P=0.011, respectively).
Hence, the subsequent analysis has been carried out for each of these cities to capture the same and chi2 test has been carried out and reported to find out the independence of responses (11) across cities.
In order to formally calculate a p-value for this difference between rates, we used the Chi2 test to quantify the extent to which the difference of mortality rates between areas was statistically significant at an a level of 0.05.
There was no evidence of meaningful heterogeneity in this meta-analysis: I2 was 0%, the chi2 test for heterogeneity was not significant and the confidence intervals from individual studies overlapped.
For the observed genotype frequencies in control group of each included study, Chi2 test was applied for the HWE.
Table 6 The GMM Estimates: Dependent Variable (GDP per Capita Growth) Variables (1) (2) (3) RD 0.0206 * 0.0455 *** 0.0461 *** (0.0120) (0.0167) (0.0176) OPN 0.0414 ** 0.0705 ** (0.0204) (0.0327) T/GDP 0.0475 * 0.0592 * (0.0274) (0.0312) HC 0.0505 *** 0.0515 *** (0.0159) (0.0190) INF -0.00966 * (0.00529) BD Constant 0.0658 ** 0.113 * 0.112 * (0.0263) (0.0642) (0.0640) Observations 37 37 37 R-squared 0.247 0.409 0.408 Wald Chi2 Test 3.92 10.31 11.67 Normality Test 0.97(0.61) 0.70(0.71) 0.71(0.70) Endogeneity Test 0.0685 0.0885 0.0711 Over Identification test 0.7070 0.9423 0.9638 D.
ES--d effect size [27]; U--Mann-Whitney U test; [Chi.sup.2] --frequencies, Chi2 test (Pearson); t--paired two-sample t test.
The relationship between the level of physical activity and socio-demographic characteristics (gender, age, body mass index (BMI), marital status, education, nature of work, income) was evaluated using the Chi2 test. Strength of the relationship between socio-demographic characteristics and conformity to the WHO health-related standards was expressed by the odds ratio (OR), which was determined with a 95% confidence interval.