Multiple regression is a statistical method used to analyze relationships between variables. It allows us to predict one variable based on other related variables, while taking into account their individual effects and interactions with each other. In multiple regression models, we have at least two independent variables (predictors) that are believed to influence the dependent variable (target). The goal of this method is to find a linear combination of these predictor variables that can best explain or predict the behavior of the target variable.
In simpler terms, if there are several factors that could affect an outcome and we want to understand how much each factor contributes to the overall result, multiple regression models can help us determine the relative importance of each independent variable in predicting the dependent variable. For example, a researcher might use this method to study the relationship between academic performance (dependent variable) and various factors such as class attendance, test scores, and GPA (independent variables). By analyzing these relationships through multiple regression models, we can better understand how each factor influences overall academic performance.
Overall, multiple regression models are a powerful tool for understanding complex relationships between different variables and predicting outcomes based on those relationships.