Follow @ProbabilityPuz This write up is about the simple linear regression and ways to make it robust to outliers and non linearity. The linear regression method is a simple and powerful method. It is powerful because it helps compress a lot of information through a simple straight line. The complexity of the problem is vastly simplified. However being so simple comes with its set of limitations. For example, the method assumes that after a fit is made, the differences between the predicted and actual values are normally distributed. In reality, we rarely run into such ideal conditions. Almost always there is non-normality and outliers in the data that makes fitting a straight line insufficient. However there are some tricks you could do to make it better. Statistics: A good book to learn statistics As an example data set consider some dummy data shown in the table/chart below. Notice, value 33 is an outlier. When charted. you can see there is some non-linearity in the