Power Analysisfor poisson Regression

Power Analysisfor poisson Regression - Solve mathematical problems with step-by-step solutions.

Power: Poisson Regression

Power depends on expected rate differences (effect size), α, covariates, dispersion, and sample size or exposure time.

Key Inputs

  • Effect size (rate ratio or log coefficient)
  • α (significance level)
  • Sample size / exposure

Example

Detecting a rate ratio of 1.3 at α=0.05 typically requires larger n than for 1.5, given the same variance and exposure.

FAQs

Overdispersion?
Use robust methods or quasi-Poisson; it reduces effective power.

Offsets?
Include log exposure in the model to scale rates.

How to use the Power Analysisfor poisson Regression

Follow these steps to get accurate results with the power analysisfor poisson regression.

  1. 1

    Enter your values

    Fill in the required input fields above. Units can be changed where available.

  2. 2

    Click Calculate

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  3. 3

    Review your results

    View the computed outputs and use related calculators for deeper analysis.