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	मल्टीपल रिग्रेशन के लिए शक्ति विश्लेषण (Power Analysis for Multiple Regression)	पियर्सन सहसंबंध के लिए सांख्यिकीय शक्ति विश्लेषण
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Hypothesis test	Hypothesis test
उद्भव वर्ष	1988	1988
प्रवर्तक	Jacob Cohen	Jacob Cohen
प्रकार	A priori sample size determination	Sample size / power determination
मौलिक स्रोत	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832
उपनाम	regression power analysis, sample size estimation regression, f² power analysis, Güç Analizi — Regresyon	Korelasyon Güç Analizi, power analysis for r, sample size for correlation
संबंधित	4	4
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.	Correlation power analysis is a pre-study calculation that determines how many participants are needed — or how much statistical power an existing sample provides — for a Pearson correlation test. Formalised by Jacob Cohen in his landmark 1988 text, it uses the expected correlation coefficient r directly as the effect size, so researchers can plan studies that are neither underpowered nor wastefully large.