📈 Advanced Topics in Linear Regression

Introduction to Linear Regression

Linear regression is a fundamental statistical method used to model the relationship between a dependent variable and one or more independent variables. It's widely used for making predictions and inferring trends.

In-Depth Analysis

While simple linear regression deals with a single predictor variable, multiple linear regression involves multiple predictors. This allows for more complex models that can capture a wider range of phenomena. 🐸

Mathematical Formulation

The general linear regression model is represented by the equation:

Y = β₀ + β₁X₁ + β₂X₂ + ... + β_nX_n + ε

Where:

• Y - Dependent variable
• X_i - Independent variables
• β_i - Coefficients
• ε - Error term

Applications and Case Studies

Explore various applications of linear regression in real-world scenarios. Some interesting case studies include predicting housing prices and analyzing marketing campaign effectiveness.

Related Links and Further Reading

• Multiple Regression
• Logistic Regression
• Time Series Analysis