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https://github.com/djeada/statistics-notes
This repository contains notes, explanations, and code snippets related to essential statistics concepts and techniques. The materials cover a range of topics, from basic probability and descriptive statistics to more advanced concepts like hypothesis testing and confidence intervals.
https://github.com/djeada/statistics-notes
confidence-intervals geostatistics hypothesis-testing kriging-models probability-distribution statistics time-series
Last synced: about 4 hours ago
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This repository contains notes, explanations, and code snippets related to essential statistics concepts and techniques. The materials cover a range of topics, from basic probability and descriptive statistics to more advanced concepts like hypothesis testing and confidence intervals.
- Host: GitHub
- URL: https://github.com/djeada/statistics-notes
- Owner: djeada
- License: mit
- Created: 2021-05-09T00:35:27.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-01-09T19:59:12.000Z (27 days ago)
- Last Synced: 2025-01-09T20:33:38.414Z (27 days ago)
- Topics: confidence-intervals, geostatistics, hypothesis-testing, kriging-models, probability-distribution, statistics, time-series
- Language: Jupyter Notebook
- Homepage: https://adamdjellouli.com/articles/statistics_notes
- Size: 3.8 MB
- Stars: 4
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Statistics
This repository contains notes, explanations, and code snippets related to essential statistics concepts and techniques. The materials cover a range of topics, from basic probability and descriptive statistics to more advanced concepts like hypothesis testing and confidence intervals.
## Requirements
The programming examples in this repository are primarily implemented in Python due to its simplicity, versatility, and the robustness of its scientific computing ecosystem. The code exploits various widely-used libraries such as NumPy for numerical computing, SciPy for advanced scientific computations, and pandas for data manipulation and analysis. As a result, a basic understanding of Python programming and its scientific libraries would be beneficial for comprehending and utilizing the code snippets.
To ensure you can run the code snippets and notebooks seamlessly, please make sure your environment fulfills the Python dependencies. We recommend setting up a virtual environment to avoid any package conflicts.
You can set up a virtual environment using the following steps:
```bash
# Create a virtual environment
python3 -m venv env
```
To activate the virtual environment, the command differs based on your operating system:
```bash
# On Windows, use:
env\Scripts\activate
# On Unix or MacOS, use:
source env/bin/activate
```
Once the virtual environment is activated, install the necessary packages using pip:
```bash
pip install -r requirements.txt
```
Now, you should be ready to run the code in this repository.
```bash
# Here's an example of how you can run a Python script
python scripts/basic_concepts/basic_concepts.py
```
Remember to replace 'scripts/basic_concepts/basic_concepts.py' with the actual name of the script you wish to run.
When you're done working, you can deactivate the virtual environment by simply running the deactivate command.
```bash
deactivate
```
## Topics
### Basic Concepts
Concept | Notes | Implementation | Examples |
--------------------------------------- | ------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- |
Introduction to Statistics | 
| 
| 
|
Descriptive Statistics | | | |
Introduction to Probability | | N/A | N/A |
Geometric Probability | | | |
Axioms of Probability | | N/A | N/A |
Conditional Probability and Independence | | N/A | N/A |
Bayes Theorem | | | |
Probability Trees | | N/A | N/A |
Total Probability | | N/A | N/A |
Bayesian vs Frequentist | | | |
### Probability Distributions
| Concept | Notes | Implementation | Examples |
|---------------------------------------|---------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|
| Introduction to Distributions | | | |
| Central Limit Theorem | | | |
| Beta Distribution | | | |
| Chi-Square Distribution | | | |
| Exponential Distribution | | | |
| F Distribution | | | |
| Gamma Distribution | | | |
| Log-Normal Distribution | | | |
| Normal Distribution | | | |
| Student t Distribution | | | |
| Uniform Distribution | | | |
| Binomial Distribution | | | |
| Geometric Distribution | | | |
| Negative Binomial Distribution | | | |
| Poisson Distribution | | | |
### Hypothesis Testing and Confidence Intervals
| Concept | Notes | Implementation | Examples |
|--------------------------|-----------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|
| Null Hypothesis | | | |
| Hypothesis Testing | | | |
| Type I and Type II Errors | | | |
| Confidence Intervals | | | |
| Multiple Comparisons | | | |
| Analysis of Variance (ANOVA) | | | |
| Analysis of Categorical Data | | | |
| Resampling | | | |
### Correlation and Regression
Concept | Notes | Implementation | Examples
------ | ----- | -------------- | --------
Correlation | | |
Covariance | | |
Simple Linear Regression | | |
Multiple Regression | | |
Logistic Regression | | |
Metrics | | |
### Time Series Analysis
| Concept | Notes | Implementation | Examples |
|-----------------------------|-----------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|
| Time Series | | | |
| Seasonality and Trends | | | |
| Series | | | |
| Difference Equations | | | |
| Stationarity | | | |
| Invertibility | | | |
| Backward Shift Operator | | | |
| Random Walk | | | |
| Forecasting | | | |
| Autoregressive Models | | | |
| Moving Average Models | | | |
| Autocorrelation Function | | | |
| Autocovariance Function | | | |
| Yule-Walker Equations | | | |
### Spatial Statistics
Concept | Notes | Implementation | Examples
------ | ----- | -------------- | --------
Point Processes | | |
Spatial Autocorrelation | | |
Geostatistics | | |
## How to Contribute
We encourage contributions that enhance the repository's value. To contribute:
1. Fork the repository.
2. Create your feature branch (`git checkout -b feature/AmazingFeature`).
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`).
4. Push to the branch (`git push origin feature/AmazingFeature`).
5. Open a Pull Request.
## References
### Online Courses and Educational Platforms
- [Harvard University's Introduction to Probability](https://projects.iq.harvard.edu/stat110)
- [edX: Fundamentals of Statistics](https://www.edx.org/course/fundamentals-of-statistics)
### Books and eBooks
- [Think Bayes by Allen Downey](https://allendowney.github.io/ThinkBayes2/)
- [SpringerLink: An Introduction to Statistical Learning](https://link.springer.com/book/10.1007/978-1-4614-7138-7)
- [SpringerLink: The Elements of Statistical Learning](https://link.springer.com/book/10.1007/978-0-387-21736-9)
### Resources and Cheat Sheets
- [Probability Cheatsheet on GitHub](https://github.com/wzchen/probability_cheatsheet)
- [Allen Downey's Blog on Probability and Bayesian Stats](http://allendowney.blogspot.com/2016/06/there-is-still-only-one-test.html)
- [Saylor Academy: Introductory Statistics](https://saylordotorg.github.io/text_introductory-statistics/index.html)
- [Statistical Learning with Sparsity by Hastie, Tibshirani, and Wainwright](https://hastie.su.domains/CASI/)
- [Statistics How To: Probability and Statistics Main Index](https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/probability-main-index/)
### Video Lectures and Playlists
- [Oxford Playlist on Probability and Statistics](https://youtube.com/playlist?list=PL4d5ZtfQonW0B3qW24yAj1u1SuOvgKfP5&si=8nQpv13gbZEWuuqe)
## License
This project is licensed under the [MIT License](LICENSE) - see the LICENSE file for details.