Fitting a trend line to data
In this section, we’ll explore the process of fitting a trend line to a dataset and the techniques that can be used to minimize error and maximize accuracy.
The art of fitting a line to data is all about finding the line that best represents the underlying pattern or trend. But how do we define “best?” The answer lies in minimizing the error between the predicted values generated by our trend line and the actual data points. The most commonly used method for achieving this is known as the least squares technique.
Imagine you’ve drawn a line through your data points, and for each point, you measure the vertical distance between the actual data point and the corresponding point on the line. This distance is known as the “residual” or the “error.” The goal of the least squares technique is to find the line that minimizes the sum of the squared residuals. Squaring the residuals is crucial because...
