เปรียบเทียบวิธี

	ค่า V ของ Cramér	การทดสอบแบบเอ็กแซกต์ของฟิชเชอร์	การถดถอยโลจิสติก
สาขาวิชา	สถิติศาสตร์	สถิติศาสตร์	สถิติการวิจัย
ตระกูล	Hypothesis test	Hypothesis test	Process / pipeline
ปีกำเนิด	1946	1922	1958
ผู้ริเริ่ม	Harald Cramér	R. A. Fisher	David Roxbee Cox
ประเภท	Nonparametric association measure	Exact test of independence for categorical data	Method
แหล่งต้นตำรับ	Cramér, H. (1946). Mathematical Methods of Statistics. Princeton University Press. ISBN: 978-0691080420	Fisher, R. A. (1922). On the interpretation of chi-squared from contingency tables, and the calculation of P. Journal of the Royal Statistical Society, 85(1), 87–94.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
ชื่อเรียกอื่น	cramers v, cramer v, phi coefficient (r×c), Cramer's V (İlişki Kuvveti)	Fisher-Irwin test, exact test of independence, Fisher'ın Kesin Testi	logit model, binomial logistic regression, LR
ที่เกี่ยวข้อง	3	2	3
สรุป	Cramer's V is a nonparametric effect-size statistic that measures the strength of association between two categorical variables on a scale from 0 to 1. Introduced by the Swedish mathematician Harald Cramér in his 1946 work Mathematical Methods of Statistics, it generalises the phi coefficient to tables of any size, making it the standard companion statistic to the chi-square test.	Fisher's exact test is a nonparametric exact-probability test of independence for small-sample contingency tables, introduced by R. A. Fisher in 1922. Rather than relying on a large-sample approximation, it computes the exact probability of the observed table directly from the hypergeometric distribution.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.