Porovnat metody

	Přesná inferenční statistika založená na randomizaci	Jackknife Resampling	Regrese metodou ordinárních nejmenších čtverců (OLS)
Obor	Statistika	Statistika	Ekonometrie
Rodina	Regression model	Regression model	Regression model
Rok vzniku	1935	1956	2019
Tvůrce	Ronald A. Fisher	Quenouille (1956); reviewed by Miller (1974)	Wooldridge (textbook treatment); classical least squares
Typ	Exact permutation-based inference	Resampling / bias and variance estimation	Linear regression
Původní zdroj	Fisher, R. A. (1935). The Design of Experiments. Oliver & Boyd.	Quenouille, M. H. (1956). Notes on Bias in Estimation. Biometrika, 43(3/4), 353-360.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Další názvy	fisher randomization test, permutation inference, exact randomization test, randomizasyon çıkarımı (fisher exact randomization)	leave-one-out resampling, Quenouille-Tukey jackknife, delete-one jackknife, Jackknife Yeniden Örnekleme	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Příbuzné	5	5	5
Shrnutí	Randomization inference, introduced by Ronald A. Fisher in The Design of Experiments (1935), computes an exact p-value by evaluating a test statistic across all possible treatment assignments under Fisher's sharp null hypothesis. It is regarded as the gold standard for analysing designed experiments because its validity rests on the known assignment mechanism rather than on distributional assumptions.	The jackknife is a classical resampling method that estimates the bias and variance of a statistic by systematically recomputing it with one observation left out at a time. Introduced by Quenouille in 1956 and later reviewed by Miller in 1974, it predates the bootstrap and remains a simple, deterministic tool for assessing estimator stability.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).