Method of Least Squares

Method of Least Squares
The method of least squares is a way of finding the line that best fits the data (i.e. finding a line that goes through or as close to as many of the data point as possible). This ‘line of best fit’ is found by ascertaining which line, of all of the possible lines that could be drawn, results in the least amount of difference between the observed data points and the line.
When any line is fitted to a set of data there will be small differences between the values predicted by the line and the data that were actually observed. We are interested in the vertical differences between the line and the actual data because we are using the line to predict values of Y from values of the X-variable.

Although some data points fall exactly on the line, others lie above and below the line, indicating that there is a difference between the model fitted to these data and the data collected. Some of these differences are positive (they are above the line, indicating that the model underestimates their value) and some are negative (they are below the line, indicating the model overestimates their value). These differences are called residuals.

Leave a Comment

Your email address will not be published. Required fields are marked *

Skip to content