Statistical forecast fitting model

Minitab computes three measures of accuracy of the fitted model MAPE, MAD, and MSD for each of the simple forecasting and smoothing methods. For all three measures, the smaller the value, the better the fit of the model. These statistics are used to compare the fits of the different methods. 

The book presents a well-defined procedure for adding or subtracting independent variables to the model variable and covers how to apply statistical forecasting methods to the serially correlated data characteristically found in clinical and pharmaceutical settings. The standalone chapters allow you to pick and choose which chapter to read first and hone in on the information that fits your immediate needs. Each example is presented in computer software format. The author uses MiniTab in the book but supplies instructions that are easily adapted for SAS and SPSSX, making the book applicable to individual situations. 

Another aspect of matching output to user needs involves presentation of results in a statistical framework—namely, as frequency distributions of concentrations. The output of deterministic models is not directly suited to this task, because it provides a single sample point for each run. Analytic linkages can be made between observed frequency distributions and computed model results. The model output for a particular set of meteorologic conditions can be on the frequency distribution of each station for which observations are available in sufficient sample size. If the model is validated for several different points on the frequency distribution based on today s estimated emission, it can be used to fit a distribution for cases of forecast emission. The fit can be made by relating characteristics of the distribution with a specific set of model predictions. For example, the distribution could be assumed to be log-normal, with a mean and standard deviation each determined by its own function of output concentrations computed for a standardized set of meteorologic conditions. This, in turn, can be linked to some effect on people or property that is defined in terms of the predicted concentration statistics. The diagram below illustrates this process ... 

Forecast with the goal in mind. Evaluate the fit of the demand models and statistical engines. 

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