Linear regression
The name linear regression will tell you all you need to know about it—the regression part tells you this method performs regression analysis, and the linear part tells you the method assumes linear relationships between attributes.
To find a possible relationship between attributes, linear regression assumes and models a universal equation that relates the target (the dependent attribute) to the predictors (the independent attributes). This equation is depicted here:
This equation uses a parameter approach. In this equation N stands for the number of predictors shows the linear regression universal equation.
The working of linear regression is very simple. The method first estimates the βs so that the equation fits the data best, and then uses the estimated βs for prediction.
Let's learn this method with an example. We will continue solving the number of MSU applications in the following example.
