stats-goodness-of-fit-tutorial

Simple notebooks to explain the theory and implementation of goodness-of-fit tests

Contents:

• 1. Introductions and histograms
  • Introduction
  • Choosing histogram bins
• 2. Fitting with method of moments
  • Mathematical derivation
  • Implementation with Python
• 3. Fitting with maximum likelihood estimation
  • Mathematical derivation
  • Confidence intervals for MLE estimates for large samples
• 4. Goodness of fit testing
  • Fitting a distribution using Fitter library
  • Implementing goodness of fit tests
    • Sum of squared errors
    • AIC/BIC
    • KL divergence
    • KS test
    • Chi square test

How to run locally

• git clone this repository
• pip install -r requirements.txt to install dependencies

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How to contribute

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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References

Exponential distribution basics

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