Statistics (scipy.stats)

In this tutorial, we discuss many, but certainly not all, features of scipy.stats. The intention here is to provide a user with a working knowledge of this package. We refer to the reference manual for further details.

Note: This documentation is work in progress.

• Probability distributions
  • Continuous Statistical Distributions
  • Discrete Statistical Distributions
  • Getting help
  • Common methods
  • Random number generation
  • Shifting and scaling
  • Shape parameters
  • Freezing a distribution
  • Broadcasting
  • Specific points for discrete distributions
  • Fitting distributions
  • Performance issues and cautionary remarks
  • Remaining issues
  • Building specific distributions
• Sample statistics and hypothesis tests
  • Analysing one sample
  • Comparing two samples
  • Resampling and Monte Carlo Methods
• Universal Non-Uniform Random Number Sampling in SciPy
  • Introduction
  • Basic concepts of the Interface
  • Generators in scipy.stats.sampling
  • References
• Kernel density estimation
  • Univariate estimation
  • Multivariate estimation
• Multiscale Graph Correlation (MGC)
• Quasi-Monte Carlo
  • Calculate the discrepancy
  • Using a QMC engine
  • Making a QMC engine, i.e., subclassing QMCEngine
  • Guidelines on using QMC