ABSTRACT
In Chapter 8, we covered correlation techniques. Where there is a very strong linear correlation between two variables and one can be hypothesized to depend on the other, we can model the relation between the two variables as a straight line that fits the correlated data when presented on a scatter plot. This line is called a regression line or a line of best fit and allows us to predict values of the dependent variable where we know the value of the independent variable. The process of producing the model is referred to as bivariate regression. It is also possible to model a dependent variable in terms of more than one independent variable; this is called multiple linear regression. This chapter covers both bivariate regression and multiple linear regression, the assumptions of the techniques and how the models produced can be used for predictive purposes.
