/regression-analysis-examples

Detailed implementation of various regression analysis models and concepts on real dataset.

Primary LanguageJupyter Notebook

Regression Analysis Examples

Detailed implementation of various regression analysis models and concepts on real datasets.

Regression Models Covered

  • Simple Linear Regression Model
  • Multiple Linear Regression Model
  • Count Based Dataset Regression Models - Poisson, Negative Binomial, Generalised Poisson
  • Quantile Regression Model
  • Ordinary Least Squares (OLS) Estimate Based Regression Model
  • Generalised Least Squares (GLS) Estimate Based Regression Model

Regression Concepts Covered

You will find implementation of below concepts which can be used for your reference:

  • Log Returns
  • Ordinary Least Squares (OLS) Estimate
  • Generalised Least Squares (GLS) Estimate
  • MultiCollinearity
  • Variance Inflation Factor (VIF)
  • Standardized Residuals
  • Studentized Residuals
  • Leverages
  • Outliers
  • Influential Cases
  • Cook's Distance
  • Regression Diagnostics
  • Residual Diagnostics
  • Feature Engineering
  • Goodness of Fit - Deviance and Pearson Chi-Squared
  • Regression Parameters Significant Tests
  • Heteroskedasticity
  • White's Heteroskedasticity Consistent (HC) Estimator
  • Heteroskedasticity & Autocorrelation Consistent (HAC) Estimator
  • Q-Q Plot
  • LOESS smoothed estimate
  • AutoCorrelation
  • Simple Linear Regression Model
  • Multiple Linear Regression Model
  • Poisson Regression Model
  • Binomial Regression Model
  • Generalised Poisson Regression Model
  • Quantile Regression Model

General Regression Analysis Steps

(It's overall guidance not strict, only for overview)

  1. Load Dataset

  2. Visualise dataset (if possible)

  3. Feature Engineering (if required)

  4. Define Regression Model

    • Response variable (y)
    • Explanatory variables or features (X)
    • Residual assumption (start with Gauss Markov Assumption)

  5. Feature Selection

    • check for MultiCollinearity and take action
    • Apply PCA

    Goal: features should be independent

  6. Fit Regression Model (starts with OLS Estimate)

  7. Regression Diagnostics

    • Regression parameters significance using t-test & generalised linear F-Test.
    • Leverages, Outliers and Influential cases
    • R-Squared
    • For Count based dataset, goodness of fit (Deviance and Pearson Chi-Squared)

    Goal: All regression parameters are statistically significant, High R-Squared, model shouldn't be affected by influential cases.

  8. Residual Diagnostics

    • Plots:
      • Residual plot only
      • Residual vs fitted values
      • Q-Q plot
      • ACF plot
    • Tests:
      • Homoskedasticity
      • Normal Distribution
      • Auto-Correlation

    Goal: Residual should be white noise.

  9. If required delete influential cases, modify model in terms of explanatory variables and | or residual assumption. Go to 6-7-8 step again.

  10. Once we have satisfied model, do forecasting + performance metrics on test dataset.