The method starts by sampling a set of start values within the defined ranges of possible values for all input variables and calculating the subsequent model outcome. The second step changes the values for one variable (all other inputs remaining at their start values) and calculates the resulting change in model outcome compared to the first run. Next, the values for another variable are changed (the previous variable is kept at its changed value and all other ones kept at their start values) and the resulting change in model outcome compared to the second run is calculated. This goes on until all input variables are changed. This procedure is repeated r times (where r is usually taken between 5 and 15), each time with a different set of start values, which leads to a number of r (k + 1) runs, where k is the number of input variables. Such number is very efficient compared to more demanding methods for sensitivity analysis.