A number of statistical techniques are used to estimate economic variables of interest to a manager. In some cases, statistical estimation techniques employed are simple. In other cases, they are much more advanced. Thus, a manager may want to know the average price received by his competitors in the industry, as well as the standard deviation (a measure of variation across units) of the product price under consideration. In this case, the simple statistical concepts of mean (average) and standard deviation are used. Estimating a relationship among variables requires a more advanced statistical technique. For example, a firm may want to estimate its cost function, the relationship between a cost concept and the level of output. A firm may also want to know the demand function of its product, that is, the relationship between the demand for its product and different factors that influence it. The estimates of costs and demand are usually based on data supplied by the firm. The statistical estimation technique employed is called regression analysis, and is used to develop a mathematical model showing how a set of variables are related. This mathematical relationship can also be used to generate forecasts. An automobile industry example can be used for the purpose of illustrating the forecasting method that employs simple regression analysis. Suppose a statistician has data on sales of American-made automobiles in the United States for the last 25 years. He or she has also determined that the sale of automobiles is related to the real disposable income of individuals. The statistician also has available the time series (for the last 25 years) on real disposable income. Assume that the relationship between the time series on sales of American-made automobiles and the real disposable income of consumers is actually linear and it can thus be represented by a straight line. A fairly rigorous mathematical technique is used to find the straight line that most accurately represents the relationship between the time series on auto sales and disposable income. 7.FORECASTING. Forecasting is a method or a technique used to predict many future aspects of a business or any other operation. While the term "forecasting" may appear to be rather technical, planning for the future is a critical aspect of managing any organizationusiness, nonprofit, or otherwise. In fact, the long-term success of any organization is closely tied to how well the management of the organization is able to foresee its future and develop appropriate strategies to deal with the likely future scenarios. There are many forecasting techniques available to the person assisting the business in planning its sales. For illustration, consider a forecasting method in which a statistician forecasting future values of a variable of business interestales, for example examines the cause-and-effect relationships of this variable with other relevant variables, such as the level of consumer confidence, changes in consumers' disposable incomes, the interest rate at which consumers can finance their excess spending through borrowing, and the state of the economy represented by the percentage of the labor force unemployed. Thus, this category of forecasting techniques uses past time series on many relevant variables to forecast the volume of sales in the future. Under this forecasting technique, a regression equation is estimated to generate future forecasts (based on the past relationship among variables).