5.1.8 Points to be considered when selecting a prediction model

Many models used at varying levels of sophistication depend on the quality of information required to select suitable methods. With an increase in the sophistication, the complexity of model also increases. This leads to an increasing number of uncertain parameters. The error in estimating parameters is carried over through the model, thus producing a less accurate model.

Box 5.2 Modified mathematical model

Modified mathematical model If the model calculations are linear, with the inputs only being multiplied by constants and being added or subtracted, it is possible to calculate the probability distribution of the outputs from those of the inputs. For example, suppose that a process has inputs a, b, and c, all normally distributed with means Ma, Mb, and Mc, and standard deviations Sa, Sb, and Sc. If these are combined to form: g = Pa · a + Pb · b + Pc · c, with Pa, Pb, and Pc being factors or numbers, the output g will also be normally distributed with mean: Mg = Pa · Ma + Pb · Mb, + Pc · Mc and standard deviation:

Table 5.1 Generation of random numbers for calculating means and standard deviations

Where limited resources are available, decisions have to be made about information needs for different effects and therefore about the allocation of these resources between the different effects. If specially designed methods are already available this will help to reduce the use of resources.