STATISTICA Process Analysis is comprised of two modules which include comprehensive implementations of process capability
analysis, gage repeatability and reproducibility analysis, Weibull analysis, sampling plans, and variance components for
random effects, each of which is described in the following sections.
STATISTICA Process Analysis is compatible with Windows 2000 and Windows XP.
Process capability analysis.STATISTICA Process Analysis includes a comprehensive selection of options for computing
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, the user 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. The program will compute 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. An option is also available for automatically
fitting all distributions, and choosing the distribution that best fits the data.
Designs for Gage Repeatability/Reproducibility (R&R) Analyses. Repeatability/reproducibility experiments with single or
multiple trials can be generated and analyzed. The data for the R&R analysis can be arranged in raw-data format, or tabulated in a
standard R&R data sheet format (as used in many publications of the American Society for Quality Control, and manuals of
the Automotive Action Group). Results include estimates of the components of variance (repeatability or equipment variation,
operator or appraiser variation, part variation, operator-by-part variation, operators-by-trials, parts-by-trials,
operators-by-parts-by-trials), which can be computed based on the range method, or the ANOVA table. If based on the ANOVA table,
confidence intervals for the variance components will also be estimated. Additional statistics for the variance components include
the percent-of-tolerance, process variation, and total variation. The program will also compute descriptive statistics by
operator/part, range and sigma charts by operators/parts, box-and-whisker plots, and the summary R&R plot. Comprehensive
selections of methods for estimating variance components for random effects are also available in the designated STATISTICA Variance Components module (included in this application), and the
General Linear Models module available in STATISTICA Advanced Linear/Non-Linear Models.
Weibull analysis. The Weibull analysis options provide powerful graphical techniques for exploiting the
power and generalizability of the Weibull distribution. The user can produce Weibull probability plots and estimate the parameters of the distribution,
along with confidence intervals for reliability. Probability plots can be computed for complete, single-censored, and
multiple-censored data, and parameters can be estimated from hazard plots of failure orders. Estimation methods include Maximum
Likelihood (for complete and censored data), weighting factors based on linear estimation techniques for complete and
single-censored data, and Modified Moment Estimators which are unbiased with respect to both the mean and variance. Confidence
intervals are computed for the shape, scale, and location parameters, as well as for the percentiles. The program includes
graphical goodness of fit tests, and the Hollander-Proschan, Mann-Scheuer-Fertig, and Anderson-Darling tests of goodness of fit.
Note that the Generalized Linear Models module of STATISTICA Advanced Linear/Non-Linear Models provides options for fitting generalized linear
models from the exponential family of distributions to normal and non-normal data.
Sampling plans. Fixed
and sequential sampling plans can be generated for normal and binomial means, or Poisson frequencies. Results include the sample
sizes, operating characteristic (OC) curves, plots of the sequential plans with or without data, expected
(H0/H1) run lengths, etc. Note that STATISTICA Power Analysis also
provides options for computing required sample sizes and power estimates for a large number of research designs (e.g, ANOVA) and
data types (e.g., for binary counts, censored failure time data, etc.).
STATISTICA Process Analysis is comprised of two modules which include comprehensive implementations of process capability
analysis, gage repeatability and reproducibility analysis, Weibull analysis, sampling plans, and variance components for
random effects, each of which is described in the following sections.
Process capability analysis. STATISTICA Process Analysis includes a comprehensive selection of options for computing
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, the user 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. The program will compute 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. An option is also available for automatically
fitting all distributions, and choosing the distribution that best fits the data.
Designs for Gage Repeatability/Reproducibility (R&R) Analyses. Repeatability/reproducibility experiments with single or
multiple trials can be generated and analyzed. The data for the R&R analysis can be arranged in raw-data format, or tabulated in a
standard R&R data sheet format (as used in many publications of the American Society for Quality Control, and manuals of
the Automotive Action Group). Results include estimates of the components of variance (repeatability or equipment variation,
operator or appraiser variation, part variation, operator-by-part variation, operators-by-trials, parts-by-trials,
operators-by-parts-by-trials), which can be computed based on the range method, or the ANOVA table. If based on the ANOVA table,
confidence intervals for the variance components will also be estimated. Additional statistics for the variance components include
the percent-of-tolerance, process variation, and total variation. The program will also compute descriptive statistics by
operator/part, range and sigma charts by operators/parts, box-and-whisker plots, and the summary R&R plot. Comprehensive
selections of methods for estimating variance components for random effects are also available in the designated STATISTICA Variance Components module (included in this application), and the
General Linear Models module available in STATISTICA Advanced Linear/Non-Linear Models.
Weibull analysis. The Weibull analysis options provide powerful graphical techniques for exploiting the
power and generalizability of the Weibull distribution. The user can produce Weibull probability plots and estimate the parameters of the distribution,
along with confidence intervals for reliability. Probability plots can be computed for complete, single-censored, and
multiple-censored data, and parameters can be estimated from hazard plots of failure orders. Estimation methods include Maximum
Likelihood (for complete and censored data), weighting factors based on linear estimation techniques for complete and
single-censored data, and Modified Moment Estimators which are unbiased with respect to both the mean and variance. Confidence
intervals are computed for the shape, scale, and location parameters, as well as for the percentiles. The program includes
graphical goodness of fit tests, and the Hollander-Proschan, Mann-Scheuer-Fertig, and Anderson-Darling tests of goodness of fit.
Note that the Generalized Linear Models module of STATISTICA Advanced Linear/Non-Linear Models provides options for fitting generalized linear
models from the exponential family of distributions to normal and non-normal data.
Sampling plans. Fixed
and sequential sampling plans can be generated for normal and binomial means, or Poisson frequencies. Results include the sample
sizes, operating characteristic (OC) curves, plots of the sequential plans with or without data, expected
(H0/H1) run lengths, etc. Note that STATISTICA Power Analysis also
provides options for computing required sample sizes and power estimates for a large number of research designs (e.g, ANOVA) and
data types (e.g., for binary counts, censored failure time data, etc.).
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