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Linear regression models the linear relationship between a dependent variable and one or more independent variables.
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♾ Linear Regression

Summary: Linear Regression is a supervised learning algorithm used for predicting a continuous outcome variable based on one or more predictor variables. The goal is to find the best-fitting straight line that minimizes the sum of the squared differences between observed and predicted values. ​​​​​​​

Linear Regression sub-types

1. Simple Linear Regression
2. Multiple Linear Regression
3. Polynomial Linear Regression
4. Ridge Regression (L2 Regularization)
5. Lasso Regression (L1 Regularization)
6. Elastic Net Regression
7. Robust Regression
8. Quantile Regression
9. Logistic Regression (for binary outcomes)
10. Poisson Regression (for count data)
11. Ordinal Regression (for ordinal data)
12. Negative Binomial Regression (for over-dispersed count data)
13. Cox Proportional Hazards Model (for survival analysis)
14. Generalized Linear Models (GLM)
15. Hierarchical Linear Models (HLM) / Multi-level Regression

Key Concepts:

  • Continuous Outcome
  • Least Squares
  • Coefficients


Applications:

  • Predicting future asset price action
  • Forecasting implementations and adoptions