قارن الطرق

	الاستدلال العشوائي الدقيق لفيشر (Fisher Exact Randomization Inference)	انحدار الكميات (الصيغ غير المعلمية)	انحدار المربعات الصغرى العادية (OLS)
المجال	الإحصاء	الإحصاء	الاقتصاد القياسي
العائلة	Regression model	Regression model	Regression model
سنة النشأة	1935	1978	2019
صاحب الطريقة	Ronald A. Fisher	Koenker & Bassett	Wooldridge (textbook treatment); classical least squares
النوع	Exact permutation-based inference	Quantile regression (nonparametric variants)	Linear regression
المصدر التأسيسي	Fisher, R. A. (1935). The Design of Experiments. Oliver & Boyd.	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
الأسماء البديلة	fisher randomization test, permutation inference, exact randomization test, randomizasyon çıkarımı (fisher exact randomization)	quantile regression, median regression, distribution-free quantile regression, Kantil Regresyon (Nonparametric Varyantlar)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
ذات صلة	5	5	5
الملخص	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.	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).