### 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

y=a+bx

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**:

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