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3. Production Process Characterization
3.4. Data Analysis for PPC


Building Models

Black box models When we develop a data collection plan we build black box models of the process we are studying like the one below:
In our data collection plan we drew process model pictures black box model
Numerical models are explicit representations of our process model pictures In the Exploring Relationships section, we looked at how to identify the input/output relationships through graphical methods. However, if we want to quantify the relationships and test them for statistical significance, we must resort to building mathematical models.
Polynomial models are generic descriptors of our output surface There are two cases that we will cover for building mathematical models. If our goal is to develop an empirical prediction equation or to identify statistically significant explanatory variables and quantify their influence on output responses, we typically build polynomial models. As the name implies, these are polynomial functions (typically linear or quadratic functions) that describe the relationships between the explanatory variables and the response variable.
Physical models describe the underlying physics of our processes On the other hand, if our goal is to fit an existing theoretical equation, then we want to build physical models. Again, as the name implies, this pertains to the case when we already have equations representing the physics involved in the process and we want to estimate specific parameter values.
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