Part 2 book “Pearson new international edition” has contents: Multiple regression analysis, regression with time series data, regression with time series data, judgmental forecasting and forecast adjustments, the Box-Jenkins (ARIMA) methodology. | MULTIPLE REGRESSION ANALYSIS In simple linear regression, the relationship between a single independent variable and a dependent variable is investigated. The relationship between two variables frequently allows one to accurately predict the dependent variable from knowledge of the independent variable. Unfortunately, many real-life forecasting situations are not so simple. More than one independent variable is usually necessary in order to predict a dependent variable accurately. Regression models with more than one independent variable are called multiple regression models. Most of the concepts introduced in simple linear regression carry over to multiple regression. However, some new concepts arise because more than one independent variable is used to predict the dependent variable. Multiple regression involves the use of more than one independent variable to predict a dependent variable. SEVERAL PREDICTOR VARIABLES As an example, return to the problem in which sales volume of gallons of milk is forecast from knowledge of price per gallon. Mr. Bump is faced with the problem of making a prediction that is not entirely accurate. He can explain almost 75% of the differences in gallons of milk sold by using one independent variable. Thus, 25% 11 - r22 of the total variation is unexplained. In other words, from the sample evidence Mr. Bump knows 75% of what he must know to forecast sales volume perfectly. To do a more accurate job of forecasting, he needs to find another predictor variable that will enable him to explain more of the total variation. If Mr. Bump can reduce the unexplained variation, his forecast will involve less uncertainty and be more accurate. A search must be conducted for another independent variable that is related to sales volume of gallons of milk. However, this new independent, or predictor, variable cannot relate too highly to the independent variable (price per gallon) already in use. If the two independent variables
