Hypothesis testing and other testing methods

# hypothetical - Hypothesis and Statistical Testing in Python

Python library for conducting hypothesis and other group comparison tests.

## Installation

The best way to install `hypothetical` is through `pip`.

```pip install hypothetical
```

For those interested, the most recent development version of the library can also be installed by cloning or downloading the repo.

```git clone git@github.com:aschleg/hypothetical.git
cd hypothetical
python setup.py install
```

## Available Methods

### Analysis of Variance

• One-way Analysis of Variance (ANOVA)
• One-way Multivariate Analysis of Variance (MANOVA)

### Contingency Tables and Related Tests

• Chi-square test of independence
• Fisher's Exact Test
• McNemar's Test of paired nominal data
• Cochran's Q test

### Descriptive Statistics

• Kurtosis
• Skewness
• Mean Absolute Deviation
• Pearson Correlation
• Spearman Correlation
• Covariance
• Several algorithms for computing the covariance and covariance matrix of sample data are available
• Variance
• Several algorithms are also available for computing variance.
• Simulation of Correlation Matrices
• Multiple simulation algorithms are available for generating correlation matrices.

### Critical Value Tables and Lookup Functions

• Chi-square statistic
• r (one-sample runs test and Wald-Wolfowitz runs test) statistic
• Mann-Whitney U-statistic
• Wilcoxon Rank Sum W-statistic

### Hypothesis Testing

• Binomial Test
• t-test
• paired, one and two sample testing

### Nonparametric Methods

• Friedman's test for repeated measures
• Kruskal-Wallis (nonparametric equivalent of one-way ANOVA)
• Mann-Whitney (two sample nonparametric variant of t-test)
• Mood's Median test
• One-sample Runs Test
• Wald-Wolfowitz Two-Sample Runs Test
• Sign test of consistent differences between observation pairs
• Wald-Wolfowitz Two-Sample Runs test
• Wilcoxon Rank Sum Test (one sample nonparametric variant of paired and one-sample t-test)

### Normality and Goodness-of-Fit Tests

• Chi-square one-sample goodness-of-fit
• Jarque-Bera test

### Post-Hoc Analysis

• Tukey's Honestly Significant Difference (HSD)
• Games-Howell (nonparametric)

• Add noise to a correlation or other matrix
• Tie Correction for ranked variables
• Contingency table marginal sums
• Contingency table expected frequencies
• Runs and count of runs

## Goal

The goal of the `hypothetical` library is to help bridge the gap in statistics and hypothesis testing capabilities of Python closer to that of R. Python has absolutely come a long way with several popular and amazing libraries that contain a myriad of statistics functions and methods, such as `numpy`, `pandas`, and `scipy`; however, it is my humble opinion that there is still more that can be done to make Python an even better language for data and statistics computation. Thus, it is my hope with the `hypothetical` library to build on top of the wonderful Python packages listed earlier and create an easy-to-use, feature complete, statistics library. At the end of the day, if the library helps a user learn more about statistics or get the information they need in an easy way, then I consider that all the success I need!

## Requirements

• Python 3.5+
• `numpy>=1.13.0`
• `numpy_indexed>=0.3.5`
• `pandas>=0.22.0`
• `scipy>=1.1.0`
• `statsmodels>=0.9.0`

MIT

## Project details

This version 0.3.0 0.2.1 0.1.0

Uploaded `source`
Uploaded `py2` `py3`