Regression analysis as input for Monte Carlo simulation

The input required for Monte Carlo simulation, as described in Chapter 2, consists, for each expected value of a variable, of three estimates:

1. Pessimistic estimate, defined as having a probability of 0.10 that the reality will be worse than that;

2. Best guess;

3. Optimistic estimate, defined as having a probability of 0.10 that the reality is better than that.

The estimates can be provided by experts, but also by a regression analysis of data related to the past. The processing of the experts experiences to arrive at their estimates can be seen as an informal regression analysis taking place in their brains.

If formal regression analysis is used, as input for Monte Carlo simulation, then:

1. The best guess = x, y (value on the regression line);

2. Pessimistic or optimistic estimate = best guess plus or minus 1.28 x standard deviation from the regression analysis (line 7 in Figure 4.13). See Figure 4.14.

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