The other RSS equation, that of predicted output mean, has a term dependent on 2^nd derivatives that is initially non-intuitive:

Why is that second term there?

The other RSS equation, that of predicted output mean, has a term dependent on 2^nd derivatives that is initially non-intuitive:

Why is that second term there?

A few posts ago, I explained the nature of transfer functions and response surfaces and how they impact variational studies when non-linearities are concerned. Now that we have the context of the RSS equations in hand, let us examine the behavior of transfer functions more thoroughly.

A few posts ago, I explained the nature of transfer functions and response surfaces and how they impact variational studies when non-linearities are concerned. Now that we have the context of the RSS equations in hand, let us examine the behavior of transfer functions more thoroughly.

As stated before, the first derivative of the transfer function with respect to a particular input quantifies how sensitive the output is to that input. However, it is important to recognize that Sensitivity does not equal Sensitivity Contribution. To assign a percentage variation contribution from any one input, one must look towards the RSS output variance (σ_Y²) equation: