Jump to content
  • Spotfire Statistica® Process Analysis


    This afticle computes maximum-likelihood parameter estimates for those distributions, and it provides numerous options for evaluating the fit of the respective distribution to the data, including the frequency distribution with observed and expected frequencies, the Kolmogorov-Smirnov d statistic, histograms, Probability-Probability (P-P) plots, and Quantile-Quantile (Q-Q) plots. Options are also available for automatically fitting all distributions and choosing the distribution that best fits the data.

    The Process Analysis module contains different analytic procedures for:

    • Sampling Plans
    • Process (Machine) Capability Analysis and Non-Normal Distributions
    • Gage Repeatability and Reproducibility
    • Weibull and Reliability/Failure Time Analysis
    • Making Weibull Paper
    • Cause-and-Effect Diagrams (Ishikawa, Fishbone)

    Statistica provides the ability to compute process capability indices for grouped and ungrouped data (e.g., Cp, Cr, Cpk, Cpl, Cpu, K, Cpm, Pp, Pr, Ppk, Ppl, Ppu), normal/distribution-free tolerance limits, and corresponding process capability plots (histogram with process ranges, specification limits, normal curve). In addition, instead of these normal distribution indices and statistics, you can choose estimates (e.g., Cpk, Cpl, Cpu based on the percentile method) based on general non-normal distributions (Johnson and Pearson curve fitting by moments), as well as all other common continuous distributions including the Beta, Exponential, Extreme Value (Type I, Gumbel), Gamma, Log-Normal, Rayleigh, and Weibull distributions.

    This module computes maximum-likelihood parameter estimates for those distributions, and it provides numerous options for evaluating the fit of the respective distribution to the data, including the frequency distribution with observed and expected frequencies, the Kolmogorov-Smirnov d statistic, histograms, Probability-Probability (P-P) plots, and Quantile-Quantile (Q-Q) plots. Options are also available for automatically fitting all distributions and choosing the distribution that best fits the data.

    Process capability indices consistent and in compliance with DIN (Deutsche Industrie Norm) 55319 and ISO 21747 are available.

    Note: Sampling plans are discussed in detail in Duncan (1974) and Montgomery (1985). Most process capability procedures (and indices) were  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).

    Standard references and textbooks describing Weibull Analysis techniques include Lawless (1982), Nelson (1990), Lee (1980, 1992), and Dodson (1994). Note that very similar statistical procedures are used in the analysis of survival data and in Lee's book (Lee, 1992) to primarily address biomedical research applications. An excellent overview with many examples of engineering applications is provided by Dodson (1994).


    User Feedback

    Recommended Comments

    There are no comments to display.


×
×
  • Create New...