In the case of multiple linear regression, two more independent variables or explanatory variables show a linear relationship with the target or dependent variables. Most of the linearly describable phenomena in nature are captured by multiple linear regression. For example, the price of any item depends on the quantity being purchased, the time of the year, and the number of items available in the inventory. For instance, the price of a bottle of wine depends primarily on how many bottles you bought. Also, the price is a bit higher during festivals such as Christmas. Moreover, if there are a limited number of bottles in the inventory, the price is likely to go even higher. In this case, the price of wine is dependent on three variables: quantity, time of year, and stock quantity. This type of relationship can be captured using multiple linear regression. 

The equation for multiple linear regression is generally given as follows:

Here, Y is the dependent variable and X_is is the independent variable.