Effect size in multiple regression

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In multiple regression analysis, effect size is crucial for determining the strength of the relationship between predictors and the outcome variable. For a model aiming for an R² greater than 0.8 with 20 predictors, a larger effect size, such as 0.5, is generally preferred to ensure accuracy and significance. Smaller effect sizes, like 0.2, may not provide the desired predictive power in this context. Understanding the appropriate effect size can help in calculating the necessary sample size for robust results. Accurate modeling relies on selecting an effect size that aligns with the research goals.
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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