Effect size in multiple regression

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SUMMARY

This discussion focuses on determining the appropriate effect size for a multiple regression analysis involving 20 predictors, specifically aiming for an R² value greater than 0.8. The user seeks clarification on whether to use a small effect size (0.2) or a large effect size (0.5) to achieve an accurate model. The consensus indicates that a larger effect size is preferable for achieving a high R² value in this context. The discussion also references a sample size calculator available at StatTools for multiple regression.

PREREQUISITES
  • Understanding of multiple regression analysis
  • Familiarity with effect size concepts
  • Knowledge of R² and its significance in regression models
  • Basic proficiency in using statistical calculators, such as StatTools
NEXT STEPS
  • Research the implications of effect size in regression analysis
  • Learn how to interpret R² values in the context of model accuracy
  • Explore advanced features of the StatTools sample size calculator
  • Study the relationship between predictor variables and effect size in multiple regression
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Statisticians, data analysts, and researchers involved in regression modeling who seek to enhance the accuracy of their predictive models.

bradyj7
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Hello,

I'm using this this calculator to determine a rough sample size for a multiple regression (20 predictors).

http://www.stattools.net/SSizmreg_Pgm.php

I don't really understand the effect size?

Could somebody tell me if you are using multiple regression (with 20 predictors) and you want the regression to have an R2 > 0.8, do you want a small effect size for example 0.2 or a large effect size for example 0.5?

Basically I want a accurate model, what effect size value should I use?

Thank you
 
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