Linganisha mbinu

	Uchambuzi wa Uamuzi wa Kawaida wa Fisher	Utoaji wa Hitimisho kwa Njia ya Bootstrap	Regresheni ya Kuantili (Tofauti Zisizo za Kiwakilishi)	Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)
Nyanja	Takwimu	Takwimu	Takwimu	Ekonometriki
Familia	Regression model	Regression model	Regression model	Regression model
Mwaka wa asili	1935	1979	1978	2019
Mwanzilishi	Ronald A. Fisher	Bradley Efron	Koenker & Bassett	Wooldridge (textbook treatment); classical least squares
Aina	Exact permutation-based inference	Resampling-based inference	Quantile regression (nonparametric variants)	Linear regression
Chanzo asilia	Fisher, R. A. (1935). The Design of Experiments. Oliver & Boyd.	Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26.	Koenker, R. & Bassett, G. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Majina mbadala	fisher randomization test, permutation inference, exact randomization test, randomizasyon çıkarımı (fisher exact randomization)	bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı	quantile regression, median regression, distribution-free quantile regression, Kantil Regresyon (Nonparametric Varyantlar)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Zinazohusiana	5	5	5	5
Muhtasari	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.	Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples.	Quantile regression, introduced by Koenker and Bassett in 1978, models a chosen conditional quantile (such as the median or the 25th and 75th percentiles) of a continuous outcome rather than its mean. Its nonparametric variants fit these quantile relationships without assuming a distribution for the errors, making them a robust complement to mean-based regression on skewed data.	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).