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''Z'' = ''x y''
For any target value of ''Z'' there is an infinite number of combinations for the nominal values of ''x'' and ''y'' that will be suitable. However, if the standard deviation of ''x'' was proportional to the nominal value and the standard deviation of ''y'' was constant, then ''x'' would be reduced (to limit the random variability that will flow from the right hand side of the equation to the left hand side) and ''y'' would be increased (with no expected increase random variability because the standard deviation is constant) to bring the value of ''Z'' to the target value. By doing this, ''Z'' would have the desired nominal value and it would be expected that its standard deviation would be at a minimum: robustified.{{Citation needed}}
By taking advantage of the two principles covered above, one is able to optimise a system so that the nominal value of a systems output is kept at its desired level while also minimising the likelihood of any deviation from that nominal value. This is despite the presence of random variability within the input variables.{{Citation needed}}
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