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3. Production Process Characterization
3.1. Introduction to Production Process Characterization



There are just a few fundamental concepts needed for PPC. This section will review these ideas briefly and provide links to other sections in the Handbook where they are covered in more detail. 
Distribution(location, spread, shape) For basic data analysis, we will need to understand how to estimate location, spread and shape from the data. These three measures comprise what is known as the distribution of the data. We will look at both graphical and numerical techniques.
Process variability We need to thoroughly understand the concept of process variability. This includes how variation explains the possible range of expected data values, the various classifications of variability, and the role that variability plays in process stability and capability.
Error propagation We also need to understand how variation propagates through our manufacturing processes and how to decompose the total observed variation into components attributable to the contributing sources.
Populations and sampling It is important to have an understanding of the various issues related to sampling. We will define a population and discuss how to acquire representative random samples from the population of interest.  We will also discuss a useful formula for estimating the number of observations required to answer specific questions.
Modeling For modeling, we will need to know how to identify important factors and responses.  We will also need to know how to graphically and quantitatively build models of the relationships between the factors and responses. 
Experiments Finally, we will need to know about the basics of designed experiments including screening designs and response surface designs so that we can quantify these relationships. This topic will receive only a cursory treatment in this chapter. It is covered in detail in the process improvement chapter. However, examples of its use are in the case studies.
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