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Test document

11-10. We can draw line plots of the actual and forecast values for each observation (or time period). Such line plots are automatically drawn by most forecasting software including ExcelModules. The line plots can be used to show whether there are sizable errors in the forecast. In addition, they can be used to detect whether the forecasting model does a good job of replicating the pattern of actual values over the past few time periods. For the model to be valid, there should be no consistent under- or over-forecast seen, and forecast errors must be randomly distributed.

11-11. The correlation coefficient is a measure of the strength of the linear relationship between two variables. That is, it measures how one variable’s value is linearly related to changes in the value of the other variable. It is usually denoted by r and can be any number between and including -1 and +1. A value of +1 indicates the two variables are perfectly correlated in a positive manner (i.e., if either variable increases in value, the other one follows suit). A value of -1 indicates the two variables are perfectly correlated in a negative manner. Finally, a value of 0 indicates the two variables are not linearly correlated.

11-12. In order to use Solver to determine the optimal weights in the weighted moving average model, we set the weights as the decision variables (or Changing Cells). The objective (or Target Cell) is the measure of forecast error, such as MAD, MSE, or MAPE, which we wish to minimize. If we want to specify that the weights must add up to 1, we must include it as a constraint in the model. The only other constraint is the non-negativity constraint on the decision variables (weights). The Assume Linear Model option should not be checked in solving this problem since the formula for the objective function is nonlinear.