Metadata-Version: 2.1
Name: nbi-stat
Version: 0.8.4
Summary: Statistics tools for teaching at NBI
Home-page: https://cholmcc.gitlab.io/nbi-python/statistics/#Statistik
Author: Christian Holm Christensen
Author-email: cholmcc@gmail.com
License: GPL
Project-URL: Documentation, https://cholmcc.gitlab.io/nbi-python/statistics/nbi_stat
Project-URL: Source Code, https://gitlab.com/cholmcc/nbi-python
Keywords: Niels Bohr Institute,Physics,Statistics,Fitting,Rounding,Histogram,Random distribution,Visualizing,Tabulating
Platform: UNKNOWN
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

# Statistical tools for teaching at NBI 

This package extends some of the existing tools in
[_NumPy_](https://numpy.org) and [_SciPy_](https://scipy.org) with
some useful features designed to make life easier for the students at
the Niels Bohr Institute. 

## Topics

- Reporting scientific results, including proper rounding 
- Tabulation of data useful in Jupyter Notebooks 
- Visualisation of data in 1 and many dimensions 
- Robust calculations of sample means, variances, and covariances, for
  unweighted and weighted samples.  For weighted samples, both
  _frequency_ and _non_-frequency weights are supported. 
- Histogramming 
- Sampling of arbitrary PDFs 
- Curve fitting using 
  - Linear least squares 
  - Non-linear least squares 
  - Maximum likelihood estimates 
    - Extended 
    - Binned 
- Representation of fit confidence contours 
- Hyppthesis testing 
- Confidence intervals 
- Template fitting 
- Simultaneous fitting over regions (channels)
- Likelihood calculations 

## Examples of use 

[This
notebook](https://cholmcc.gitlab.io/nbi-python/statistics/#nbi_stat_exa)
gives examples of use. 

## Book on Statistics with Python 

The book [Statistics Overview - With
Python](https://cholmcc.gitlab.io/nbi-python/statistics/#Statistik)
lays out much of the theoretical foundation for the tools available. 

Some other notes on statistics is available from the same site, including 

- [Principle Component Analysis](https://cholmcc.gitlab.io/nbi-python/statistics/#PCA) as a more robust alternative to boosted decision trees 
- [Bootstrap and Jackknife](https://cholmcc.gitlab.io/nbi-python/statistics/#Boostrap) and why you should be careful with these estimates 
- [Coefficent of determination](https://cholmcc.gitlab.io/nbi-python/statistics/#R2) and why you shouldn't use it 

## Application Programming Interface Documentation 

The API is
[documented](https://cholmcc.gitlab.io/nbi-python/statistics/nbi_stat). 

2019 © _Christian Holm Christensen_


