Process Analysis

  • Process Capability Analysis
    Creates process capability indices for grouped (multiple sample) or ungrouped data. Process performance and capability indices are computed for grouped data. You can also fit various specific non-normal distributions (e.g., Weibull, log-normal, Beta, Gamma, etc.) as well as general non-normal distributions by moments, and compute capability indices using the percentile method.
  • Generate Gage R and R Design
    Generates a standard design for a Gage repeatability and reproducibility experiment. Results are reported in standard spreadsheet format, and in the form of a Gage R & R input data sheet.
  • Analyze Gage R and R Experiments
    Analyzes standard gage repeatability and reproducibility experiments, computes variance components, and performs percent-of-tolerance analyses. Select the variables for Operators, Parts, and Trials (optional) in exactly that order into the list of categorical predictor variables. Use the Variance Components and General Linear Models facilities to analyze complex (non-standard, non-balanced) experimental designs with random effects (to analyze variance components).
  • Gage Linearity
  • Sampling Plans
    Sampling plans for normal data (continuous) data, binomial proportions, and Poisson frequencies. Constructs simple and sequential sampling plans for user-defined alpha and beta error rates. See also the Power Analysis facilities for tools to determine sample sizes, estimate power, etc. for various statistical procedures.
  • Weibull and Reliability/Failure Time Analysis
    Fits the two and three-parameter Weibull distribution to survival or failure time data, with or without censoring. Various results graphs and spreadsheets are provided in order to evaluate the quality of the fit (goodness of fit) and to estimate the reliability and hazard functions.
  • Weibull Probability Paper
    Produces a user-defined Weibull probability plot (with probability scales); when no data are plotted but only the probability scale and the logarithmic scale for failure times is shown, such plots are also called probability paper.
  • Cause-Effect (Ishikawa, Fishbone) Diagrams
    Creates cause-effect diagrams, also referred to as Ishikawa or Fishbone charts. The cause-and-effect diagram provides an efficient summary of factors that impact a process, and hence can be used as a map to guide the overall quality improvement efforts. The cause-and-effect diagram plays a central role in Six Sigma quality programs.