Statistical tests for contingency tables

G-test

In statistics, G-tests are likelihood-ratio or maximum likelihood statistical significance tests that are increasingly being used in situations where chi-squared tests were previously recommended. The general formula for G is where is the observed count in a cell, is the expected count under the null hypothesis, denotes the natural logarithm, and the sum is taken over all non-empty cells. Furthermore, the total observed count should be equal to the total expected count: where is the total number of observations. G-tests have been recommended at least since the 1981 edition of Biometry, a statistics textbook by Robert R. Sokal and F. James Rohlf. (Wikipedia).

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From playlist F Test

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G tests for categorical variables using R

In this video I take a look at two alternative tests for the chi-squared goodness of fit test and the chi-squared test of independence. These as the G tests for goodness of fit and independence. They are log-likelihood tests. The rendered HTML file is on RPubs at http://rpubs.com/juanhk

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From playlist Hypothesis Tests and Critical Values

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From playlist Computational Genomics Winter Institute 2018

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From playlist G*Power 3.1 Tutorials

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7. Stability via Frequency Response

MIT Electronic Feedback Systems (1985) View the complete course: http://ocw.mit.edu/RES6-010S13 Instructor: James K. Roberge License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT Electronic Feedback Systems (1985)

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From playlist Hypothesis Test with Two Samples

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Related pages

Mutual information | Contingency table | Statistics | Chi-squared distribution | Stata | Null hypothesis | Efficiency (statistics) | Pearson's chi-squared test | Fisher's exact test | Kullback–Leibler divergence | Likelihood-ratio test | Multinomial test | Probability distribution | Taylor series | Degrees of freedom (statistics) | Natural logarithm | Entropy (information theory) | Chi-squared test | Statistical significance