Training Module:SUMMA

Summa Six Sigma

This course covers a number of advanced statistical methods useful for individuals engaged in Six Sigma projects. The Six Sigma online training techniques described go beyond those usually presented in Six Sigma training programs. However, they help to learn Six Sigma for important situations that are often encountered when analyzing real data.

Summa Six Sigma online training module length: 2 days

Prerequisites for Summa Six Sigma online training: Attendees should learn Six Sigma standard statistical methods, including capability analysis, control charts, regression analysis, and design of experiments. Knowledge of the material covered in the BASIC, SPC1 and DOE1 modules, described on the Course Descriptions page, is sufficient.


Multivariate Capability Analysis

  • Multivariate Normal Distribution
  • Estimation of Joint Probability of Being Within Spec.
  • Multivariate Capability Indices

Multivariate Process Control

  • Hotelling's T-Squared
  • T-Squared Control Charts
  • Multivariate EWMA Control Charts
  • Generalized Variance Charts
  • Use of Principal Components Analysis or PLS with Control Ellipses

Control Charts and Capability Analysis for Nonnormal Data

  • Probability Distributions for Skewed Data
  • Probability Distributions for Data with Significant Kurtosis
  • Generalized Gamma, Generalized Logistic, Exponential Power Distributions
  • Selecting the Proper Distribution
  • Control Limits for Nonnormal Data Capability
  • Indices for Nonnormal  
  • Data Transformation Methods

Outlier Identification and Accommodation

  • Grubbs, Dixon's and Tukey's Tests
  • Accommodation Methods (Trimming, Winsorization)

Control Charts for Autocorrelated Data

  • Identifying and Estimating ARIMA Models
  • Modifying Control Limits
  • Residual Control Charts

Special Purpose Control Charts

  • Modifying Control Limits for High Cpk Processes
  • Cuscore Charts for Detecting Special Patterns
  • Toolwear Charts for Trending Data

Regression Analysis and Classification

  • Fitting Nonlinear Models
  • Discriminant Analysis
  • Bayesian Neural Networks

Multivariate Optimization

  • Multivariate Desirability Functions
  • Following the Path of Steepest Ascent

Automatic Forecasting

  • Forecasting Methods
  • Model Selection Criteria