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Analýza sily pre viacnásobnú regresiu×Mnohonásobná lineárna regresia×
OdborŠtatistikaŠtatistika
RodinaHypothesis testRegression model
Rok vzniku19881886
TvorcaJacob CohenFrancis Galton; formalized by Karl Pearson
TypA priori sample size determinationParametric linear model
Pôvodný zdrojCohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263. DOI ↗
Ďalšie názvyregression power analysis, sample size estimation regression, f² power analysis, Güç Analizi — RegresyonMLR, OLS regression, multiple regression, linear regression with multiple predictors
Príbuzné48
ZhrnutiePower 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.
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ScholarGatePorovnať metódy: Power Analysis for Regression · Multiple Linear Regression. Získané 2026-06-17 z https://scholargate.app/sk/compare