Regression is a statistical analysis that attempts to predict the effect of one or more variables on another variable. Regression analysis is often used in the business and investment world to attempt to predict the effect of certain inputs on an output.For example, an analyst may want to try to predict the effect of the price of steel on car sales, or a company may want to see if its sales can be predicted by movement in the GDP. The variable being influenced is called the dependent variable, because its value depends on the other variables. The other variables are called independent variables. A linear regression has one independent variable. When there are more than one independent variables, it is multiple regression. An example of multiple regression would be General Motors seeking to learn the relationship of interest rates, the price of steel, the price of oil, and national income on its stock price. Regression is also used to determine covariance and correlation, which are variables used in the investment world to show how much two stocks tend to move in the same direction, or in different directions. This is important information for investors who want to diversify to stocks that are not correlated to the ones they already own.