Generates a probability plot of sample data against the quantiles of a specified theoretical distribution (the normal distribution by default). probplot optionally calculates a best-fit line for the data and plots the results using Matplotlib or a given plot function.

Calculate and optionally plot probability plot correlation coefficient. The probability plot correlation coefficient (PPCC) plot can be used to determine the optimal shape parameter for a one-parameter family of distributions.

Probability distributions# SciPy has two infrastructures for working with probability distributions. This tutorial is for the older one, which has many pre-defined distributions; however, the new infrastructure can be used with most of these and has many advantages.

This module contains a large number of probability distributions, summary and frequency statistics, correlation functions and statistical tests, masked statistics, kernel density estimation, quasi-Monte Carlo functionality, and more.

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, skewnorm.pdf(x, a, loc, scale) is identically equivalent to skewnorm.pdf(y, a) / scale with y = (x - loc) / scale .

As an instance of the rv_continuous class, norm object inherits from it a collection of generic methods (see below for the full list), and completes them with details specific for this particular distribution. Notes. The probability density function for norm is:

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, t.pdf(x, df, loc, scale) is identically equivalent to t.pdf(y, df) / scale with y = (x - loc) / scale .

The probability density above is defined in the “standardized” form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, expon.pdf(x, loc, scale) is identically equivalent to expon.pdf(y) / scale with y = (x - loc) / scale .

If \(f\left(x;\boldsymbol{\theta}\right)\) is the PDF of a random-variable where \(\boldsymbol{\theta}\) is a vector of parameters ( e.g. \(L\) and \(S\)), then for a collection of \(N\) independent samples from this distribution, the joint distribution the random vector \(\mathbf{x}\) is

May 11, 2014 · Generates a probability plot of sample data against the quantiles of a specified theoretical distribution (the normal distribution by default). probplot optionally calculates a best-fit line for the data and plots the results using Matplotlib or a given plot function.