Multiple linear regression is a statistical method used to model the relationship between multiple independent variables and a single dependent variable. In simple linear regression, there is only one independent variable, but in multiple linear regression, there are two or more independent variables.

The basic idea behind multiple linear regression is to understand how changes in the independent variables are associated with changes in the dependent variable while controlling for the effects of other variables. The relationship is expressed through an equation of the form:

Y=b0+b1X1+b2X2+…+bnXn+εY=b0​+b1​X1​+b2​X2​+…+bn​Xn​+ε

Here:

• Y is the dependent variable.
• b0​ is the intercept (the value of Y when all independent variables are zero).
• b1,b2,…,bnb1​,b2​,…,bn​ are the coefficients representing the change in Y for a one-unit change in the corresponding independent variable.
• X1,X2,…,XnX1​,X2​,…,Xn​ are the independent variables.
• $\epsilon$ is the error term, representing the unobserved factors that influence Y but are not included in the model.

The goal in multiple linear regression is to estimate the coefficients b0,b1,…,bnb0​,b1​,…,bn​ that minimize the sum of the squared differences between the predicted values and the actual values of the dependent variable. This is typically done using methods like least squares estimation.

Multiple linear regression is widely used in various fields, including economics, finance, biology, and social sciences, to analyze and model relationships between variables.

For a demonstration of this analysis in SPSS, click here: https://www.youtube.com/watch?v=f8n3Kt9cvSI&list=PLtx0cY9iho28Iw0o97hVjao2NB-LLd9wT&index=2

For a demonstration of this analysis in Excel, click here: https://www.youtube.com/watch?v=F_U5m77lqMU&list=PLtx0cY9iho2-vaeRtNTz2BlEcpFwzYWGK&index=6