Process Analysis Overview

The Process Analysis module contains different analytic procedures for:

Sampling plans are discussed in detail in Duncan (1974) and Montgomery (1985); most process capability procedures (and indices) were only recently introduced to the US from Japan (Kane, 1986), however, they are discussed in three excellent hands-on books by Bhote (1988), Hart and Hart (1989), and Pyzdek (1989); detailed discussions of these methods can also be found in Montgomery (1991).

Step-by-step instructions for the computation and interpretation of capability indices are also provided in the Fundamental Statistical Process Control Reference Manual published by the ASQC (American Society for Quality Control) and AIAG (Automotive Industry Action Group, 1991; referenced as ASQC/AIAG, 1991). Repeatability and reproducibility (R & R) methods are discussed in Grant and Leavenworth (1980), Pyzdek (1989) and Montgomery (1991); a more detailed discussion of the subject (of variance estimation) is also provided in Duncan (1974).

Step-by-step instructions on how to conduct and analyze R & R experiments are presented in the Measurement Systems Analysis Reference Manual published by ASQC/AIAG (1990). In the following topics, we will briefly introduce the purpose and logic of each of these procedures (click on a topic name to go to the overview about that topic). The examples presented in the Examples section of the manual will provide additional details about these methods. Note that Statistica also includes the Variance Components and Mixed ModelANOVA/ANCOVA module, which contains numerous options for analyzing designs with random effects and for estimating components of variance. See also the section on Methods for Analysis of Variance for additional details.

Standard references and textbooks describing Weibull Analysis techniques include Lawless (1982), Nelson (1990), Lee (1980, 1992), and Dodson (1994); the relevant functions of the Weibull distribution (hazard, CDF, reliability) are also described in Weibull CDF, Reliability, and Hazard Functions. Note that very similar Statistical procedures are used in the analysis of survival data (see also the description of the Survival Analysis module), and, for example, the descriptions in Lee's book (Lee, 1992) are primarily addressed to biomedical research applications. An excellent overview with many examples of engineering applications is provided by Dodson (1994).

• Statistica Gage Linearity Overview
  
• Process Analysis - Process (Machine) Capability Analysis - Introductory Overview
  
• Overview of Time-Dependent Distribution Models
  
• Capability Ratios for True Position - Introductory Overview
  Some manufacturing processes, and the allowable tolerances that define acceptable quality, can best be summarized by the metaphor of hitting a target.
• Process Analysis Gage Repeatability and Reproducibility - Introductory Overview
  Gage repeatability and reproducibility analysis addresses the issue of precision of measurement. The purpose of repeatability and reproducibility experiments is to determine the proportion of measurement variability that is due to 1) the items or parts being measured (part-to-part variation), 2) the operator or appraiser of the gages (reproducibility), and 3) errors (unreliabilities) in the measurements over several trials by the same operators of the same parts (repeatability). In the ideal case, all variability in measurements will be due to the part-to-part variation, and only a negligible proportion of the variability will be due to operator reproducibility and trial-to-trial repeatability.
• Attribute Gage Study (Analytic Method) - Introductory Overview
  Attribute gage studies are conducted in order to assess the amount of bias and repeatability in a gage when the response is a binary (such as accept or reject) attribute variable.
• Attribute Agreement Overview
  In some situations, physical measurements made on certain aspects of quality are difficult or impossible to obtain and reliance upon subjective ratings must be employed.
• MSA Attribute Data Overview
  Many measurement systems deal with qualitative data. For example, parts coming off a production line might be considered as simply good or bad (go/no-go). Although information may be lost when measuring a quality characteristic on an attribute level, it is sometimes simpler to understand and may alleviate the need for expensive precise devices and time-consuming measurement procedures.
• Capability Analysis - Binomial and Poisson - Computational Details
  
• Weibull and Reliability/Failure Time Analysis - Introductory Overview
  

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