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STATISTICA Process Analysis is a comprehensive implementation of Process Capability analysis, Gage Repeatability and Reproducibility analysis, Weibull analysis, sampling plans, and variance components for random effects:
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STATISTICA Process Analysis is a comprehensive implementation of Process Capability analysis, Gage Repeatability and Reproducibility analysis, Weibull analysis, sampling plans, and variance components for random effects.
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, 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.
STATISTICA 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. Options are also available for automatically fitting all distributions and choosing the distribution that best fits the data.
STATISTICA additionally offers process capability indices consistent and in compliance with DIN (Deutsche Industrie Norm) 55319 and ISO 21747.
Some manufacturing processes, and the allowable tolerances that define acceptable quality, can best be summarized by the metaphor of "hitting a target." For example, when drilling holes at specific locations, the quality requirement is best defined by circles around the desired locations; every time that a hole is drilled outside the acceptable quality (circle), the respective part is rejected.
Tolerances (specification limits) defined as a circle in the two-dimensional plane are also called positional tolerances. For such processes, the standard capability values (ratios) are not appropriate, because while the process may be within-specs on each individual dimension, the respective point (in the two-dimensional plane) may be unacceptably far away from the desired goal (point).
For example, in the illustration shown, there are two points that, when considering the +/-1 USL/LSL specification limits for each dimension separately, would not be out of specs. However, if the specs are defined as a circle around the origin {0,0}, with a radius of 1, then these two points would be rejected.
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 and manuals of the Automotive Industry 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. STATISTICA 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, and the General Linear Models module available in STATISTICA Advanced Linear/Nonlinear Models.
Attribute gage studies are conducted in order to assess the amount of bias and repeatability in a gage when the response is a binary (e.g., accept or reject) attribute variable. In STATISTICA, two methods for testing bias are available: the AIAG method and the Regression method.
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. In order for the subjective measurements to be considered meaningful, there should be agreement among multiple appraisers. Rating usefulness is obtained if the appraisers agree. Use STATISTICA’s Attribute Agreement Analysis to measure the agreement of ratings given by multiple appraisers.
The MSA method for attribute data is a straightforward method that can be used to assess the accuracy of appraisers and the types of mistakes they are likely to make. Typically, samples of parts are appraised by operators as good or bad. These classifications are then compared with a reference or standard.
The Weibull analysis options provide powerful graphical techniques for exploiting the power and generalizability of the Weibull distribution. You 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. STATISTICA 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/Nonlinear Models provides options for fitting generalized linear models from the exponential family of distributions to normal and non-normal data.
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 compatible with Windows XP, Windows Vista, and Windows 7.
Native 64-bit versions and highly optimized multiprocessor versions are available.