ABSTRACT
In many business and economic situations, you will be faced with a problem where you are interested in the relationship that exists between two different random variables X and Y. This type of a relationship is known as a bivariate relationship. In the bivariate model, we are interested in predicting the value of a dependent or response variable based upon the value of one independent or explanatory variable. For example, a marketing manager may be interested in the relationship between advertising and sales. A production manager may want to predict steel production as it relates to household appliance output. A financial analyst may be interested in the bivariate relationship between investment X and its future returns Y; or an economist may look at consumer expenditure as a function of personal disposable income. Regression models are also called causal or explanatory models. In this case, forecasters use regression analysis to quantify the behavioral relationship that may exist between economic and business variables. They may use regression models to evaluate the impact of shifts in internal (company level) variables, such as discount prices and sales, and external economic factors, such as interest rates and income, on company sales.
