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https://github.com/ejw-data/stats-guide

Repo held on my local machine of useful Python stats and visualizaton tools. Tutorials being developed for DS Class.
https://github.com/ejw-data/stats-guide

Last synced: about 1 year ago
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Repo held on my local machine of useful Python stats and visualizaton tools. Tutorials being developed for DS Class.

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README

          

-- Note: Readme is not complete. Notebooks are annotated.

## Topics

1. Central Limit Theorem
1. Likelihood versus Probability
1. Standard Deviation Heuristics with Normal Distribution
1. Distribtion Comparisons (t-distribution and normal distribution)
1. Meaning of Test Statistics, t-value and z-value
1. Application of Distributions - t-tests and confidence intervals
1. Evaluation of Errors, $\alpha, \beta$
1. Normality Tests

## Packages
* Scipy
* Statsmodels
* pyMC3
* [pingouin](https://pingouin-stats.org/build/html/index.html)

## Contents

![image1](./images/non_normal_distribution_2023_04_30_82.png)

![image1](./images/central_limit_theorem_2023_04_26_47.png)

![image1](./images/likelihood_probability_2023_04_30_67.png)

![image1](./images/t_distribution_2023_04_30_79.png)

![image1](./images/t_normal_compare_2023_04_30_80.png)

![image1](./images/heuristics_2023_04_30_78.png)

![image1](./images/hypothesis_testing2_2023_10_30_45.png)



Truth about Population


Hypothesis
True Effect ($H_1$)
No Effect ($H_0$)


Reject $H_o$
True
Type I / Alpha Error


Fail to Reject $H_o$
Type II / Beta Error
True

![image1](./images/anova_data_2023_04_30_73.png)