3.   Production Process Characterization - Detailed Table of Contents  [3.]

1. Introduction to Production Process Characterization  [3.1.]
   
   1. What is PPC?  [3.1.1.]
      
   2. What are PPC Studies Used For?  [3.1.2.]
      
   3. Terminology/Concepts  [3.1.3.]
      
      1. Distribution (Location, Spread and Shape)  [3.1.3.1.]
         
         1. Controlled/Uncontrolled Variation  [3.1.3.2.1.]
            
      2. Propagating Error  [3.1.3.3.]
         
      3. Populations and Sampling  [3.1.3.4.]
         
      4. Process Models  [3.1.3.5.]
         
      5. Experiments and Experimental Design  [3.1.3.6.]
         
   4. PPC Steps  [3.1.4.]
      
      
2. Assumptions / Prerequisites  [3.2.]
   
   1. General Assumptions  [3.2.1.]
      
   2. Continuous Linear Model  [3.2.2.]
      
   3. Analysis of Variance Models (ANOVA)  [3.2.3.]
      
      1. One-Way ANOVA  [3.2.3.1.]
         
         1. One-Way Value-Splitting  [3.2.3.1.1.]
            
      2. Two-Way Crossed ANOVA  [3.2.3.2.]
         
         1. Two-way Crossed Value-Splitting Example  [3.2.3.2.1.]
            
      3. Two-Way Nested ANOVA  [3.2.3.3.]
         
         1. Two-Way Nested Value Splitting Example  [3.2.3.3.1.]
            
   4. Discrete Models  [3.2.4.]
      
      
3. Data Collection for PPC  [3.3.]
   
   1. Define Goals  [3.3.1.]
      
   2. Process Modeling  [3.3.2.]
      
   3. Define Sampling Plan  [3.3.3.]
      
      1. Identifying Parameters, Ranges and Resolution  [3.3.3.1.]
         
      2. Choosing a Sampling Scheme  [3.3.3.2.]
         
      3. Selecting Sample Sizes  [3.3.3.3.]
         
      4. Data Storage and Retrieval  [3.3.3.4.]
         
      5. Assign Roles and Responsibilities  [3.3.3.5.]
         
         
4. Data Analysis for PPC  [3.4.]
   
   1. First Steps  [3.4.1.]
      
   2. How do I tell if there are any relationships between factors and responses?  [3.4.2.]
      
      1. Exploring Main Effects  [3.4.2.1.]
         
      2. Exploring Main Effects - Part 2  [3.4.2.2.]
         
      3. Exploring First Order Interactions  [3.4.2.3.]
         
   3. How can I use my data to build mathematical models of my process?  [3.4.3.]
      
      1. Fitting Polynomial Models  [3.4.3.1.]
         
      2. Fitting Physical Models  [3.4.3.2.]
         
   4. Can I tell which factors are causing the most variation in my process?  [3.4.4.]
      
   5. How do I tell if my process is stable?  [3.4.5.]
      
   6. How do I tell if my process is capable?  [3.4.6.]
      
   7. What do I do if my assumptions are not true?  [3.4.7.]
      
      
5. Case Studies  [3.5.]