{"id":25019638,"url":"https://github.com/djeada/statistics-notes","last_synced_at":"2025-04-13T04:11:13.303Z","repository":{"id":159294921,"uuid":"365637652","full_name":"djeada/Statistics-Notes","owner":"djeada","description":"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.","archived":false,"fork":false,"pushed_at":"2025-02-07T19:29:30.000Z","size":4115,"stargazers_count":5,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-03-26T21:11:13.855Z","etag":null,"topics":["confidence-intervals","geostatistics","hypothesis-testing","kriging-models","probability-distribution","statistics","time-series"],"latest_commit_sha":null,"homepage":"https://adamdjellouli.com/articles/statistics_notes","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/djeada.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-05-09T00:35:27.000Z","updated_at":"2025-02-13T12:39:35.000Z","dependencies_parsed_at":null,"dependency_job_id":"498ec3a3-ed94-443e-aa1a-932104737517","html_url":"https://github.com/djeada/Statistics-Notes","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FStatistics-Notes","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FStatistics-Notes/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FStatistics-Notes/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/djeada%2FStatistics-Notes/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/djeada","download_url":"https://codeload.github.com/djeada/Statistics-Notes/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248661704,"owners_count":21141450,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["confidence-intervals","geostatistics","hypothesis-testing","kriging-models","probability-distribution","statistics","time-series"],"created_at":"2025-02-05T11:51:18.007Z","updated_at":"2025-04-13T04:11:13.253Z","avatar_url":"https://github.com/djeada.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Statistics\n\nThis 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.\n\n## Requirements\n\nThe 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.\n\nTo 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.\n\nYou can set up a virtual environment using the following steps:\n\n```bash\n# Create a virtual environment\npython3 -m venv env\n```\n\nTo activate the virtual environment, the command differs based on your operating system:\n\n```bash\n# On Windows, use:\nenv\\Scripts\\activate\n\n# On Unix or MacOS, use:\nsource env/bin/activate\n```\n\nOnce the virtual environment is activated, install the necessary packages using pip:\n\n```bash\npip install -r requirements.txt\n```\n\nNow, you should be ready to run the code in this repository.\n\n```bash\n# Here's an example of how you can run a Python script\npython scripts/basic_concepts/basic_concepts.py\n```\n\nRemember to replace 'scripts/basic_concepts/basic_concepts.py' with the actual name of the script you wish to run.\n\nWhen you're done working, you can deactivate the virtual environment by simply running the deactivate command.\n\n```bash\ndeactivate\n```\n\n## Topics\n\n### Basic Concepts\n\nConcept                                 | Notes                                                                                                         | Implementation                                                                                                | Examples                                                                                                  |\n--------------------------------------- | ------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- |\nIntroduction to Statistics             | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/basic_concepts/introduction_to_statistics.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/basic_concepts/population_sample.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/basic_concepts/variables_and_data.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/basic_concepts/introduction_to_statistics.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\nDescriptive Statistics | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/basic_concepts/descriptive_statistics.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/basic_concepts/averages.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/basic_concepts/frequency_tables_and_histograms.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/basic_concepts/quartiles.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/basic_concepts/standard_deviation.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/basic_concepts/descriptive_statistics.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\nIntroduction to Probability             | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/basic_concepts/introduction_to_probability.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | N/A |  N/A  |\nGeometric Probability                   | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/basic_concepts/geometric_probability.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/basic_concepts/geometric_probability.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/basic_concepts/geometric_probability.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\nAxioms of Probability                   | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/basic_concepts/axioms_of_probability.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | N/A | N/A |\nConditional Probability and Independence | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/basic_concepts/conditional_probability_and_independence.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | N/A  | N/A |\nBayes Theorem                           | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/basic_concepts/bayes_theorem.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/basic_concepts/venn_diagram_bayes_theorem.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/basic_concepts/bayes_theorem.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\nProbability Trees                        | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/basic_concepts/probability_tree.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | N/A  | N/A |\nTotal Probability                       | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/basic_concepts/total_probability.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | N/A | N/A |\nBayesian vs Frequentist                | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/basic_concepts/bayesian_vs_frequentist.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/basic_concepts/bayesian_vs_frequentist.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/basic_concepts/bayesian_vs_frequentist.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n\n### Probability Distributions\n\n| Concept                               | Notes                                                                                                   | Implementation                                                                                        | Examples                                                                                              |\n|---------------------------------------|---------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|\n| Introduction to Distributions         | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/probability_distributions/introduction_to_distributions.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/probability_distributions/introduction_to_distributions.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/notebooks/probability_distributions/introduction_to_distributions.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Central Limit Theorem                 | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/probability_distributions/central_limit_theorem.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/probability_distributions/central_limit_theorem.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/notebooks/probability_distributions/central_limit_theorem.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Beta Distribution                    | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/probability_distributions/continuous_distributions/beta_distribution.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/probability_distributions/beta_distribution.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/notebooks/probability_distributions/beta_distribution.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Chi-Square Distribution              | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/probability_distributions/continuous_distributions/chi_square_distribution.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/probability_distributions/chi_square_distribution.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/notebooks/probability_distributions/chi_square_distribution.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Exponential Distribution             | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/probability_distributions/continuous_distributions/exponential_distribution.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/probability_distributions/exponential_distribution.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/notebooks/probability_distributions/exponential_distribution.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| F Distribution                       | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/probability_distributions/continuous_distributions/f_distribution.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/probability_distributions/f_distribution.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/notebooks/probability_distributions/f_distribution.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Gamma Distribution                   | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/probability_distributions/continuous_distributions/gamma_distribution.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/probability_distributions/gamma_distribution.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/notebooks/probability_distributions/gamma_distribution.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Log-Normal Distribution              | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/probability_distributions/continuous_distributions/log_normal_distribution.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/probability_distributions/log_normal_distribution.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/notebooks/probability_distributions/log_normal_distribution.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Normal Distribution                  | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/probability_distributions/continuous_distributions/normal_distribution.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/probability_distributions/normal_distribution.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/notebooks/probability_distributions/normal_distribution.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Student t Distribution               | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/probability_distributions/continuous_distributions/student_t_distribution.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/probability_distributions/student_t_distribution.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/notebooks/probability_distributions/student_t_distribution.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Uniform Distribution                 | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/probability_distributions/continuous_distributions/uniform_distribution.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/probability_distributions/uniform_distribution.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/notebooks/probability_distributions/uniform_distribution.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Binomial Distribution                | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/probability_distributions/discrete_distributions/binomial_distribution.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/probability_distributions/binomial_distribution.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/notebooks/probability_distributions/binomial_distribution.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Geometric Distribution               | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/probability_distributions/discrete_distributions/geometric_distribution.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/probability_distributions/geometric_distribution.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/notebooks/probability_distributions/geometric_distribution.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Negative Binomial Distribution       | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/probability_distributions/discrete_distributions/negative_binomial_distribution.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/probability_distributions/negative_binomial_distribution.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/notebooks/probability_distributions/negative_binomial_distribution.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Poisson Distribution                 | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/probability_distributions/discrete_distributions/poisson_distribution.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/scripts/probability_distributions/poisson_distribution.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/main/notebooks/probability_distributions/poisson_distribution.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n\n### Hypothesis Testing and Confidence Intervals\n\n| Concept                  | Notes                                                                                               | Implementation                                                                                        | Examples                                                                                              |\n|--------------------------|-----------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|\n| Null Hypothesis           | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/statistical_inference/null_hypothesis.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/statistical_inference/null_hypothesis.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/statistical_inference/statistical_inference.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Hypothesis Testing        | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/statistical_inference/hypothesis_testing.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/statistical_inference/p_value.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/statistical_inference/hypothesis_testing.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Type I and Type II Errors | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/statistical_inference/type_i_and_type_ii_errors.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/statistical_inference/type_i_and_type_ii_errors.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/statistical_inference/type_i_and_type_ii_errors.md.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Confidence Intervals      | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/statistical_inference/confidence_intervals.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/statistical_inference/confidence_intervals.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/statistical_inference/confidence_intervals.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Multiple Comparisons      | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/statistical_inference/multiple_comparisons.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/statistical_inference/multiple_comparisons.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/statistical_inference/multiple_comparisons.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Analysis of Variance (ANOVA) | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/statistical_inference/analysis_of_variance.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/statistical_inference/analysis_of_variance.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/statistical_inference/analysis_of_variance.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Analysis of Categorical Data | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/statistical_inference/analysis_of_categorical_data.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/statistical_inference/analysis_of_categorical_data.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/statistical_inference/analysis_of_categorical_data.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Resampling                | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/statistical_inference/resampling.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/statistical_inference/resampling.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/statistical_inference/resampling.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n\n### Correlation and Regression\n\nConcept | Notes | Implementation | Examples\n------ | ----- | -------------- | --------\nCorrelation | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/correlation_and_regression/correlation.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/correlation_and_regression/correlation.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/correlation_and_regression/correlation.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e\nCovariance | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/correlation_and_regression/covariance.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/correlation_and_regression/covariance.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/correlation_and_regression/covariance.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e\nSimple Linear Regression | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/correlation_and_regression/simple_linear_regression.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/correlation_and_regression/linear_regression.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/correlation_and_regression/linear_regression.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e\nMultiple Regression | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/correlation_and_regression/multiple_regression.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/correlation_and_regression/multiple_regression.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/correlation_and_regression/multiple_regression.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e\nLogistic Regression | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/correlation_and_regression/logistic_regression.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/correlation_and_regression/logistic_regression.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/correlation_and_regression/logistic_regression.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e\nMetrics | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/correlation_and_regression/metrics.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/correlation_and_regression/metrics.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/correlation_and_regression/metrics.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e\n\n### Time Series Analysis\n\n| Concept                     | Notes                                                                                               | Implementation                                                                                        | Examples                                                                                              |\n|-----------------------------|-----------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|\n| Time Series                  | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/time_series_analysis/time_series.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/time_series_analysis/time_series_analysis.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/time_series_analysis/time_series_analysis.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Seasonality and Trends       | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/time_series_analysis/seasonality_and_trends.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/time_series_analysis/seasonality.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/time_series_analysis/seasonality.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Series                       | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/time_series_analysis/series.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/time_series_analysis/series.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/time_series_analysis/series.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Difference Equations         | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/time_series_analysis/difference_equations.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/time_series_analysis/difference_equations.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/time_series_analysis/difference_equations.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Stationarity                 | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/time_series_analysis/stationarity.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/time_series_analysis/stationarity.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/time_series_analysis/stationarity.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Invertibility                | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/time_series_analysis/invertibility.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/time_series_analysis/invertibility.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/time_series_analysis/invertibility.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Backward Shift Operator      | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/time_series_analysis/backward_shift_operator.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/time_series_analysis/backward_shift_operator.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/time_series_analysis/backward_shift_operator.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Random Walk                  | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/time_series_analysis/random_walk.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/time_series_analysis/random_walk.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/time_series_analysis/random_walk.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Forecasting                  | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/time_series_analysis/forecasting.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/time_series_analysis/forecasting.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/time_series_analysis/forecasting.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Autoregressive Models        | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/time_series_analysis/autoregressive_models.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/time_series_analysis/autoregressive_model.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/time_series_analysis/autoregressive_model.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Moving Average Models        | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/time_series_analysis/moving_average_models.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/time_series_analysis/moving_average_models.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/time_series_analysis/moving_average_models.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Autocorrelation Function     | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/time_series_analysis/autocorrelation_function.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/time_series_analysis/autocorrelation_function.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/time_series_analysis/autocorrelation_function.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Autocovariance Function      | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/time_series_analysis/autocovariance_function.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/time_series_analysis/autocovariance_function.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/time_series_analysis/autocovariance_function.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n| Yule-Walker Equations        | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/time_series_analysis/yule_walker_equations.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/time_series_analysis/yule_walker_equations.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/time_series_analysis/yule_walker_equations.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e |\n\n### Spatial Statistics\n\nConcept | Notes | Implementation | Examples\n------ | ----- | -------------- | --------\nPoint Processes | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/spatial_statistics/point_processes.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/spatial_statistics/point_processes.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/spatial_statistics/point_processes.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e\nSpatial Autocorrelation | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/spatial_statistics/spatial_autocorrelation.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/spatial_statistics/spatial_autocorrelation.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/spatial_statistics/spatial_autocorrelation.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e\nGeostatistics | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notes/spatial_statistics/geostatistics.md\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/markdown.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/scripts/spatial_statistics/spatial_statistics.py\"\u003e\u003cimg src=\"https://img.icons8.com/color/344/python.png\" height=\"50\" /\u003e\u003c/a\u003e | \u003ca href=\"https://github.com/djeada/Statistics-Notes/blob/master/notebooks/spatial_statistics/geostatistics.ipynb\"\u003e\u003cimg src=\"https://img.icons8.com/fluency/344/jupyter.png\" height=\"50\" /\u003e\u003c/a\u003e\n\n## How to Contribute\n\nWe encourage contributions that enhance the repository's value. To contribute:\n\n1. Fork the repository.\n2. Create your feature branch (`git checkout -b feature/AmazingFeature`).\n3. Commit your changes (`git commit -m 'Add some AmazingFeature'`).\n4. Push to the branch (`git push origin feature/AmazingFeature`).\n5. Open a Pull Request.\n\n## References\n\n### Online Courses and Educational Platforms\n- [Harvard University's Introduction to Probability](https://projects.iq.harvard.edu/stat110)\n- [edX: Fundamentals of Statistics](https://www.edx.org/course/fundamentals-of-statistics)\n\n### Books and eBooks\n- [Think Bayes by Allen Downey](https://allendowney.github.io/ThinkBayes2/)\n- [SpringerLink: An Introduction to Statistical Learning](https://link.springer.com/book/10.1007/978-1-4614-7138-7)\n- [SpringerLink: The Elements of Statistical Learning](https://link.springer.com/book/10.1007/978-0-387-21736-9)\n\n### Resources and Cheat Sheets\n- [Probability Cheatsheet on GitHub](https://github.com/wzchen/probability_cheatsheet)\n- [Allen Downey's Blog on Probability and Bayesian Stats](http://allendowney.blogspot.com/2016/06/there-is-still-only-one-test.html)\n- [Saylor Academy: Introductory Statistics](https://saylordotorg.github.io/text_introductory-statistics/index.html)\n- [Statistical Learning with Sparsity by Hastie, Tibshirani, and Wainwright](https://hastie.su.domains/CASI/)\n- [Statistics How To: Probability and Statistics Main Index](https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/probability-main-index/)\n\n### Video Lectures and Playlists\n- [Oxford Playlist on Probability and Statistics](https://youtube.com/playlist?list=PL4d5ZtfQonW0B3qW24yAj1u1SuOvgKfP5\u0026si=8nQpv13gbZEWuuqe)\n\n## License\n\nThis project is licensed under the [MIT License](LICENSE) - see the LICENSE file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdjeada%2Fstatistics-notes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdjeada%2Fstatistics-notes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdjeada%2Fstatistics-notes/lists"}