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.
American Heritage® Dictionary of the English Language, Fifth Edition. Copyright © 2016 by Houghton Mifflin Harcourt Publishing Company. Published by Houghton Mifflin Harcourt Publishing Company. All rights reserved.

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
Collins English Dictionary – Complete and Unabridged, 12th Edition 2014 © HarperCollins Publishers 1991, 1994, 1998, 2000, 2003, 2006, 2007, 2009, 2011, 2014

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]
Random House Kernerman Webster's College Dictionary, © 2010 K Dictionaries Ltd. Copyright 2005, 1997, 1991 by Random House, Inc. All rights reserved.
References in periodicals archive ?
The Z and chi-square tests are statistically more significant and practical to issue conclusions in auditing activities, especially when applied to a large volume of data (KRAKAR; ZGELA, 2009).
Further I2 statistics is calculated through the difference between the observed count and expected count as usual for all types of chi-square tests. In our case there is only one degree of freedom, so Yates correction for continuity was also applied.
A series of chi-square tests of independence were performed on each triadic element, separately, in relation to the discrete dimensions of parental abuse (see Table 1).
Chi-square tests (Preacher, 2001) were utilized for testing for pairwise differences of the medians of the different developmental stages in the four different oxygen levels on a specific day.
It emphasizes common research and analysis methods and techniques and covers reading and understanding existing research and analytics; collecting data, including descriptive and historical and philosophical research method and design, qualitative or naturalistic inquiry, Delphi study, experimental design, case studies, feasibility and profitability studies, and grants and contracts; and using analytics to interpret information, including Microsoft Excel software, descriptive statistics, correlation, regression, t-tests, analysis of variance (ANOVA), and chi-square tests, in addition to protecting participants and research data.
of female employees) Top Middle Lower Private Sector 3 Nil 01 14 15 Public Sector 55 01 152 57 210 Private Sector NA Nil 04 10 14 Public Sector 22 Nil 36 60 86 Private Sector 3 Nil 01 10 11 Public Sector 10 15 07 50 72 Private Sector NA Nil 03 06 09 Public Sector NA 01 19 113 133 Total Sample Size 550 Table 2 9th Statement Crosstab Count Gender (1-male,2- female) 1 2 Total 9 (yes-1, No-2) 1 153 362 515 2 9 31 40 Total 162 393 555 Table 3 9th Statement Chi-Square Tests Value Df Asymp.
Among the topics are collecting data, confidence intervals, inference for means and proportions, chi-square tests for categorical variables, multiple regression, and probability basics.
Some methods of strengthening the common Chi-square tests. Biometrics 10(4), 417-451.
Analyses of variance (ANOVA) and chi-square tests were used to test hypotheses.
Pearson's chi-square tests and t-tests were used to compare the two samples.
Among the topics are principles, lean sigma projects, value stream mapping, introductory statistics and data, making sense of data in six sigma and lean, probability, single-population estimation, chi-square tests, linear and multiple regression, the design of experiments, and survey methods and sampling techniques.