Even though Linear regression is a useful tool, it has significant limitations. It can only be fit to datasets that has one independent variable and one dependent variable.

When we have data set with many variables, Multiple Linear Regression comes handy. While it can’t address all the limitations of Linear regression, it is specifically designed to develop regressions models with one dependent variable and multiple independent variables or vice versa.

The different variations in Multiple Linear Regression model are:

1. Multiple Regression — One dependent variable (Y), more than one Independent variables(X)

2. MultiVariate Regression — more than one dependent variables(Y), One independent variable (X)

3. MultiVariate Multiple Regression — more than 1 dependent (Y) and Independent (X) variables.

The most widely used one is Multiple regression model.

What is Multiple Linear Regression?

Multiple regression is a statistical method that aims to predict a dependent variable using multiple independent variables. It is generally used to find the relationship between several independent variables and a dependent variable.

The formula for Multiple regression model is:

Y = b1X1 + b2X2 + … + bnXn + A

Where, Y denotes the predicted value ; b1, b2, … bn are the regression coefficients, which represent the value at which the X variable changes when the Y variable changes; X1, X2, … Xn are independent variables and A is the Y intercept.

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