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
- Universal Non-Uniform Random Number Sampling in SciPy
- Kernel density estimation
- Multiscale Graph Correlation (MGC)
- Quasi-Monte Carlo
- Analysing one sample
- Comparing two samples
- Resampling and Monte Carlo Methods
- Hypothesis tests
- Bartlett’s test for equal variances
- Chi-square test
- Chi-square test of independence of variables in a contingency table
- Dunnett’s test
- Fisher’s exact test
- Fligner-Killeen test for equality of variance
- Friedman test for repeated samples
- Jarque-Bera goodness of fit test
- Kendall’s tau test
- Kurtosis test
- Levene test for equal variances
- Normal test
- Odds ratio for a contingency table
- Shapiro-Wilk test for normality
- Skewness test
- Spearman correlation coefficient