Data Science — Linear Regression

Linear regression is indeed a cornerstone of statistical modeling, widely used for prediction, forecasting, and understanding relationships in data. It models the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.

Key Concepts

• Simple Linear Regression: Involves one independent variable (predictor) and one dependent variable (response). The goal is to find the “best-fit” straight line that minimizes the difference between predicted and actual values.
• Multiple Linear Regression: Extends to multiple independent variables, allowing for more complex relationships while assuming linearity.
• Objective: Estimate parameters (slope and intercept) to predict the dependent variable based on new independent variable values.

What is Linear Regression?

Linear regression helps us understand how the value of the dependent variable changes as the independent variable(s) change. This relationship is represented by the following equation:

Y = β0 + β1X1 + β2X2 + … + ε

Where:

• Y is the dependent variable
• X1, X2, … are the independent variables
• β0, β1, β2, … are the…