Power Analysis For Linear Regression
Power Analysis For Linear Regression - Solve mathematical problems with step-by-step solutions.
Power Analysis for Linear Regression
Power depends on effect size (e.g., partial R²), α, number of predictors, and sample size.
Key Parameters
- Effect size (R² or f²)
- α (significance level)
- Predictor count
- Sample size
Example
With f²=0.15, α=0.05, k=3 predictors, n=120, power is typically > 0.8.
FAQs
Effect size types?
f² relates to incremental R² of predictors.
Multicollinearity?
High collinearity reduces effective power.
How to use the Power Analysis For Linear Regression
Follow these steps to get accurate results with the power analysis for linear regression.
- 1
Enter your values
Fill in the required input fields above. Units can be changed where available.
- 2
Click Calculate
Press the calculate button to compute results instantly in your browser.
- 3
Review your results
View the computed outputs and use related calculators for deeper analysis.
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