Multiple linear regression analysis is essentially similar to the simple linear model, with the exception that multiple independent variables are used in the model. The mathematical representation of multiple linear regression is:
Y = a + bX1 + cX2 + dX3 + ϵ
Where:
Y – Dependent variable
X1, X2, X3 – Independent (explanatory) variables
a – Intercept
b, c, d – Slopes
ϵ – Residual (error)
Multiple linear regression follows the same conditions as the simple linear model. However, since there are several independent variables in multiple linear analysis, there is another mandatory condition for the model:
Non-collinearity: Independent variables should show a minimum correlation with each other. If the independent variables are highly correlated with each other, it will be difficult to assess the true relationships between the dependent and independent variables.