Metadata-Version: 2.1
Name: nightingale
Version: 2020.1.6
Summary: Python library for simplifying statistical analysis and making it more consistent
Home-page: https://github.com/idin/nightingale
Author: Idin
Author-email: py@idin.ca
License: MIT
Description: # *Nightingale*
        
        I named this package *Nightingale* in honour of 
        [Florence Nightingale](https://en.wikipedia.org/wiki/Florence_Nightingale), 
        [The lady with the data](https://thisisstatistics.org/florence-nightingale-the-lady-with-the-data/).
        
        # Installation
        
        You can use pip to install Nightingale:
        
        ```bash
        pip install nightingale
        ```
        
        # Usage
        
        ## Population Proportion
        
        ```python
        from nightingale import get_sample_size, PopulationProportion, get_z_score
        
        print('z-score for 0.95 confidence:', get_z_score(confidence=0.95))
        print('sample size:', get_sample_size(confidence=0.95, error_margin=0.05, population_size=1000))
        print('with 10% group proportion:', get_sample_size(confidence=0.95, error_margin=0.05, population_size=1000, group_proportion=0.1))
        
        population_proportion = PopulationProportion(sample_n=239, group_proportion=0.5)
        print('error:', population_proportion.get_error(confidence=0.95))
        ```
        
        ## Ordinary Least Squares (OLS)
        
        ```python
        import pandas as pd
        import numpy as np
        from nightingale import OrdinaryLeastSquares
        
        data = pd.DataFrame({
            'x': np.random.normal(size=20, scale=5), 
            'y': np.random.normal(size=20, scale=5),
        })
        data['z'] = data['x'].values + data['y'].values + np.random.normal(size=20, scale=1)
        print('data:')
        display(data.head())
        
        ols = OrdinaryLeastSquares(data=data, formula='z ~ x + y')
        print('ols results:')
        display(ols.table)
        
        print('r-squared:', ols.r_squared)
        print('adjusted r-squared:', ols.adjusted_r_squared)
        
        print('\n', 'summary:')
        display(ols.summary)
        ```
        
        ## *ANOVA*
        
        
        # References
        z-score: https://stackoverflow.com/questions/20864847/probability-to-z-score-and-vice-versa-in-python
        
        
Platform: UNKNOWN
Requires-Python: ~=3.6
Description-Content-Type: text/markdown
