Usually, more than one independent variable influences the dependent variable. You can imagine in the above example that sales are influenced by advertising as well as other factors, such as the number of sales representatives and the commission percentage paid to sales representatives. When one independent variable is used in a regression, it is called a simple regression; when two or more independent variables are used, it is called a multiple regression.
Regression models can be either linear or nonlinear. A linear model assumes the relationships between variables are straight-line relationships, while a nonlinear model assumes the relationships between variables are represented by curved lines. In business you will often see the relationship between the return of an individual stock and the returns of the market modeled as a linear relationship, while the relationship between the price of an item and the demand for it is often modeled as a nonlinear relationship.
As you can see, there are several different classes of regression procedures, with each having varying degrees of complexity and explanatory power. The most basic type of regression is that of simple linear regression. A simple linear regression uses only one independent variable, and it describes the relationship between the independent variable and dependent variable as a straight line. This review will focus on the basic case of a simple linear regression.
How does regression work to enable prediction? View the following animation for a brief explanation of the basics of simple linear regression. The subsequent text will develop ideas mentioned in the animation.