{"id":18564587,"url":"https://github.com/avinash793/regression-analysis-examples","last_synced_at":"2025-11-01T13:30:33.646Z","repository":{"id":217325333,"uuid":"743587843","full_name":"Avinash793/regression-analysis-examples","owner":"Avinash793","description":"Detailed implementation of various regression analysis models and concepts on real dataset.","archived":false,"fork":false,"pushed_at":"2024-01-15T20:48:16.000Z","size":3721,"stargazers_count":3,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-12-26T12:09:11.181Z","etag":null,"topics":["autocorrelation","cooks-distance","feature-engineering","gls-regression-model","goodness-of-fit","heteroskedasticity","influential-cases","leverages","multicollinearity","multiple-linear-regression-model","ols-regression-model","outliers","poisson-regression-model","python3","q-q-plot","quantile-regression","regression-analysis","regression-diagnostics","residuals","simple-linear-regression"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Avinash793.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2024-01-15T14:55:28.000Z","updated_at":"2024-04-13T06:19:29.000Z","dependencies_parsed_at":"2024-01-15T19:58:57.521Z","dependency_job_id":null,"html_url":"https://github.com/Avinash793/regression-analysis-examples","commit_stats":null,"previous_names":["avinash793/regression-analysis-examples"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Avinash793%2Fregression-analysis-examples","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Avinash793%2Fregression-analysis-examples/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Avinash793%2Fregression-analysis-examples/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Avinash793%2Fregression-analysis-examples/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Avinash793","download_url":"https://codeload.github.com/Avinash793/regression-analysis-examples/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239293948,"owners_count":19615043,"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":["autocorrelation","cooks-distance","feature-engineering","gls-regression-model","goodness-of-fit","heteroskedasticity","influential-cases","leverages","multicollinearity","multiple-linear-regression-model","ols-regression-model","outliers","poisson-regression-model","python3","q-q-plot","quantile-regression","regression-analysis","regression-diagnostics","residuals","simple-linear-regression"],"created_at":"2024-11-06T22:15:46.460Z","updated_at":"2025-11-01T13:30:33.593Z","avatar_url":"https://github.com/Avinash793.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Regression Analysis Examples\n\nDetailed implementation of various regression analysis models and concepts on real datasets.  \n\n### Regression Models Covered\n* Simple Linear Regression Model\n* Multiple Linear Regression Model\n* Count Based Dataset Regression Models - Poisson, Negative Binomial, Generalised Poisson\n* Quantile Regression Model\n* Ordinary Least Squares (OLS) Estimate Based Regression Model\n* Generalised Least Squares (GLS) Estimate Based Regression Model\n\n### Regression Concepts Covered\nYou will find implementation of below concepts which can be used for your reference:\n* Log Returns\n* Ordinary Least Squares (OLS) Estimate\n* Generalised Least Squares (GLS) Estimate\n* MultiCollinearity\n* Variance Inflation Factor (VIF)\n* Standardized Residuals\n* Studentized Residuals\n* Leverages\n* Outliers\n* Influential Cases\n* Cook's Distance\n* Regression Diagnostics\n* Residual Diagnostics\n* Feature Engineering\n* Goodness of Fit - Deviance and Pearson Chi-Squared\n* Regression Parameters Significant Tests\n* Heteroskedasticity\n* White's Heteroskedasticity Consistent (HC) Estimator\n* Heteroskedasticity \u0026 Autocorrelation Consistent (HAC) Estimator\n* Q-Q Plot\n* LOESS smoothed estimate\n* AutoCorrelation\n* Simple Linear Regression Model\n* Multiple Linear Regression Model\n* Poisson Regression Model\n* Binomial Regression Model\n* Generalised Poisson Regression Model\n* Quantile Regression Model\n\n### General Regression Analysis Steps\n(It's overall guidance not strict, only for overview)\n1. Load Dataset\n2. Visualise dataset (if possible)\n3. Feature Engineering (if required)\n4. Define Regression Model\n   * Response variable (y)\n   * Explanatory variables or features (X)\n   * Residual assumption (start with Gauss Markov Assumption)\n5. Feature Selection\n   * check for MultiCollinearity and take action\n   * Apply PCA \n   \n   Goal: features should be independent\n6. Fit Regression Model (starts with OLS Estimate)\n7. Regression Diagnostics\n   * Regression parameters significance using t-test \u0026 generalised linear F-Test.\n   * Leverages, Outliers and Influential cases\n   * R-Squared \n   * For Count based dataset, goodness of fit (Deviance and Pearson Chi-Squared)\n   \n   Goal: All regression parameters are statistically significant, High R-Squared, \n   model shouldn't be affected by influential cases.\n8. Residual Diagnostics\n   * Plots:\n     * Residual plot only\n     * Residual vs fitted values\n     * Q-Q plot\n     * ACF plot\n   * Tests:\n     * Homoskedasticity\n     * Normal Distribution\n     * Auto-Correlation \n   \n   Goal: Residual should be white noise.\n9. If required delete influential cases, modify model in terms of explanatory \n   variables and | or residual assumption. Go to 6-7-8 step again.\n10. Once we have satisfied model, do forecasting + performance metrics on test dataset.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Favinash793%2Fregression-analysis-examples","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Favinash793%2Fregression-analysis-examples","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Favinash793%2Fregression-analysis-examples/lists"}