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Regression Modeling
Regression modeling is a foundational statistical technique used to quantify relationships between variables and predict outcomes from data. It's widely used across business forecasting, research, and as a building block for more advanced machine learning techniques like classification models.\ \ This path covers simple and multiple linear regression, regularized regression techniques like ridge and lasso, logistic regression for classification, and polynomial regression for nonlinear relationships. Concepts are taught independent of any specific programming language or tool.
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Regression Modeling
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What You'll Learn
- How to build and interpret simple and multiple linear regression models, logistic and count based regression models
- How to apply ridge, lasso, and elastic net regularization techniques to handle overfitting and correlated predictors
- How to model nonlinear relationships using polynomial regression, and when to consider more flexible techniques like splines and GAMs
- 1. Introduction to Regression Modeling (Video)
- 2. Regularized Regression Techniques (Video)
- 3. Logistic Regression for Classification (Video)
- 4. Nonlinear Regression Models (Video)
- Basic familiarity with statistics (mean, variance, correlation) and basic algebra is recommended.