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	मल्टीपल रिग्रेशन के लिए शक्ति विश्लेषण (Power Analysis for Multiple Regression)	बहुचर रैखिक प्रतिगमन
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Hypothesis test	Regression model
उद्भव वर्ष	1988	1886
प्रवर्तक	Jacob Cohen	Francis Galton; formalized by Karl Pearson
प्रकार	A priori sample size determination	Parametric linear model
मौलिक स्रोत	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832	Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263.
उपनाम	regression power analysis, sample size estimation regression, f² power analysis, Güç Analizi — Regresyon	MLR, OLS regression, multiple regression, linear regression with multiple predictors
संबंधित	4	8
सारांश	Power analysis for multiple regression is a pre-study procedure, formalised by Jacob Cohen (1988), that calculates the minimum sample size needed to detect a regression effect of a given size with adequate statistical power. It uses the anticipated R² (or the equivalent Cohen's f² effect size) and the number of predictors to determine how many observations must be collected before data collection begins.	Multiple linear regression (MLR) is a parametric regression model that expresses a continuous outcome as a weighted linear combination of two or more predictor variables plus a random error term. The unknown weights (regression coefficients) are estimated by ordinary least squares (OLS), which minimises the sum of squared residuals. The method traces to Francis Galton's 1886 work on hereditary stature and was placed on firm mathematical footing by Karl Pearson; Draper and Smith's 1966 textbook established it as the standard framework for applied regression.