The Idea behind Least Squares Regression
- In order to conveniently estimate the expected values of one variable based on another, we often create a mathematical model which fits, as closely as possible, the data we have collected. In Further Maths, we will only deal with linear regression, where we try to come up with a straight line that fits our data.
- In least squares regression, we try to find that “best fit” by finding a line that minimises the value of the sum of squared residuals (i.e. we take the difference between each datapoint and the line, then square each and add them all together).
- The resulting line is of the form
where y and x are the response and explanatory variables, respectively, and a and b are constants which must be determined.
- Least squares linear regression is only appropriate if: