Demand is the willingness and ability of buying a product at a given price within a specific period of time.  It is a flow concept as it has quantity and time dimensions.  Demand forecasting is not a process that involves a crystal ball.  It rather involves carrying forward past data.  The main objective of the paper is to do a theoretical review of demand forecasting, solve a demand forecasting example and to set a multiple regression example.  Price is the meeting point of demand and supply.  Elasticity of demand is defined as the percentage change in quantity demanded divided by the percentage change in price.  Ordinarily the demand curve is negatively sloped but for a given good it is positively sloped.  Forecasting is the process of estimation of unknown situations.  The forecasting methods are time series method, casual econometric methods, budgetmental methods and other methods like simulation etc.  In the forecasting problem solved the actual demand for eight quarters are 200, 250, 175, 186, 115, 285, 305 and190.  With the forecast for the other quarter of 230 and a smoothing coefficient of 0.15, it was required to forecast using the three quarter and four quarter moving averages to state the differences between the two methods.  For the case of demand forecasting by the utilization of the multiple regression models, the following data is given below: