Overview
Linear regression is the simplest and most common form of regression. It assumes a straight-line relationship between the input variables (X) and the single output variable (Y).
Equation
Y = β0 + β1X + ε (where β0 is the intercept, β1 is the slope, and ε is the error term).
Key Assumptions
- Linearity: The relationship between X and Y is linear.
- Independence: Observations are independent of each other.
- Homoscedasticity: The variance of residual errors is constant.
- Normality: The residuals are normally distributed.