Multiple linear regression is a statistical method used to model the linear relationship between a dependent variable and one or more independent variables. The main goal of multiple linear regression is to determine the relationship between the independent variables and the dependent variable and to use this relationship to make predictions about the dependent variable based on new values of the independent variables.
In multiple linear regression, the dependent variable is represented by Y and the independent variables are represented by X1, X2, X3, and so on. The relationship between Y and the independent variables is represented by a linear equation, where the coefficients (b1, b2, b3, and so on) represent the relationship between each independent variable and the dependent variable.
Multiple linear regression is a powerful tool for analyzing complex relationships between variables and is widely used in many fields, including economics, finance, marketing, and engineering. To perform a multiple linear regression, it is important to have a clear understanding of the relationship between the variables and to ensure that the data used is appropriate for the analysis.
By understanding the principles of multiple linear regression, you can analyze data and make informed decisions based on your findings.