Metadata-Version: 2.1
Name: applied-stats
Version: 0.0.2
Summary: A basic statistics module to compute MLEs / probabilities
Home-page: https://github.com/WillTirone/applied_stats
Author: William Tirone
Author-email: will.tirone1@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: scipy (>1)
Requires-Dist: numpy (>1)
Requires-Dist: matplotlib (>3)

# Statistics_Module
Creating a project to implement various statistical distributions and methods in Python.

To install and run this project, do the following:
1. Run the following to install: 

``` 
pip install applied-stats
```

2. Follow these examples to start plotting and calculating probabilities. The examples below, and several others, can also be found in the  [Demonstration Jupyter Notebook](https://github.com/WillTirone/applied-stats_examples/blob/main/Demonstration.ipynb)

3. To run the test file, from the command line enter: ```python test.py```

## Usage

### To generate some plots and calculate some probabilities: 

```python
>>> from applied_stats import continuous_distributions
>>> a = Norm_rv(0,1)
>>> a.plot_pdf()
>>> a.probability_calc()
```
![link](https://github.com/WillTirone/applied_stats/blob/main/output_images/N(0%2C1)_plot.png)

```python
>>> q = ChiSq_rv(4,crit_value=7)
>>> q.plot_pdf(cv_probability=True)
>>> q.probability_calc()
```
![link](https://github.com/WillTirone/applied_stats/blob/main/output_images/X-sqr(4).png)

### To calculate the numeric MLE of several common distributions: 

```python 
>>> from stats_tools import mle 
>>> a = [1,3,2,5,6,7,2,3,4,5]
>>> mle.binomial(a)
>>> 3.8

>>> b = [1.2,4.3,2.3,6.8,2.4,3.6]
>>> mle.exponential(b) 
>>> 3.4333333333333336
```


