*y = a + bx*

where

*y*is the variable being predicted,

*x*is the variable you're predicting it from, and

*a*and

*b*are two numbers calculated by your analysis.

*a*is the intercept of the regression line (the value of y when x is zero) and

*b*is the slope. (NB you may see some books that call them 'm' and 'c'. Very confusing, but don't worry about it too much - they're just different names for the same thing. We could call them 'Bert' and 'Ernie' if we like - they're just names.).

This equation accurately describes the trendline.

Like in correlation, regression also provides an

*r*measure, describing how well the model fits the data, except we use upper-case

*R*to show it's regression. The addition of the equation describing the line adds a lot to simple regression - it allows you to predict the value of one variable from another. Remember that

*R*^2 (r squared) tells you just how accurate these predictions are likely to be.

**analyze > regression > linear**