18th Sept 2023

A linear regression model with more than one predictor variable, often referred to as multiple linear regression, is a statistical technique used in data analysis and modeling. In this approach, the goal is to establish a linear relationship between a dependent variable and multiple independent predictor variables. Unlike simple linear regression, which involves only one predictor, multiple linear regression allows us to consider the combined impact of several factors on the dependent variable. The model estimates the coefficients for each predictor variable, representing their respective contributions to the variation in the dependent variable. By incorporating multiple predictors, this model enables a more comprehensive understanding of how various factors collectively influence the outcome, making it a valuable tool in fields such as economics, social sciences, and machine learning, where complex relationships between variables need to be explored and quantified.