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The Design Space is the multidimensional combination and interaction of input variables and process parameters leading to the expected product specifications with a controlled (i.e., high) probability.
It has two input variables and one control output variable.
Classical statistical modeling was designed for data with a few dozen input variables and sample sizes that would be considered small to moderate today.
However, as the numbers of input variables and possible associations among them increase, the model that captures these relationships becomes more complex.
With a high number of input variables and the uncertainty in these variables, it is impossible to have all the answers when embarking on a transformative change.
For Pi, let Ii be all of the input variables and Oi the output variables, and likewise for Pj.
The data consists of 162,500 records with 16 input variables and one target class variable.
This resulted in a total amount of 35 input variables and 1 output variable.
Expect input, not solutions.
A state-space model includes three types of variables: input variables, output variables and state variables.
The relationship between target variable and input variable X1 and X2 is nonlinear.
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