Multiple Linear Regression

In the last lesson, we performed simple linear regression. But it was a limited model because we only used one variable from our dataset in our predictions. Our linear model was based on the fact that there was some relationship between two variables. But the variable that we are trying to predict can have relationships with other variables too. Including other variables in our model may help us make better predictions.

Multiple Linear Regression

We can extend the simple linear regression model to a multiple linear regression model by adding more variables and parameters to the model equation. Then the prediction ($\hat{y}$) becomes:

$$\hat{y} = \theta_0 + \theta_1x_1 + \theta_2x_2 + \theta_3x_3 + .... + \theta_mx_m$$

where $x_1,x_2,x_3,...,x_n$ ...