Metadata-Version: 2.1
Name: nbi_stat
Version: 0.8.3
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
Description: # 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_
        
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
