15th Sept 2023

Today, I went through the linear regression topics i.e Simple Linear Regression and Multiple linear regression.

Simple linear regression is a statistical technique used to model the relationship between a dependent variable and a single independent variable. It provides a straightforward way to understand and quantify the linear association between two variables, with an equation in the form of Y = β0 + β1X + ε, where Y is the dependent variable, X is the independent variable, β0 is the intercept, β1 is the slope, and ε represents the error term.

On the other hand, multiple linear regression extends this concept to accommodate multiple independent variables, allowing for a more complex understanding of how several factors collectively influence the dependent variable. The equation for multiple linear regression is Y = β0 + β1X1 + β2X2 + … + βnXn + ε, where Y is the dependent variable, X1, X2, …, Xn are the independent variables, β0 is the intercept, β1, β2, …, βn are the coefficients for each independent variable, and ε represents the error term. Multiple linear regression is a powerful tool for analyzing complex relationships and making predictions based on multiple factors.