Six Sigma Data Measurement, Analysis, and Tools

Please note that Business Process Improvement Using Lean Six Sigma and Performance Metrics is a prerequisite course, and you must complete it before taking Six Sigma Data Measurement, Analysis, and Tools.

This course provides both the theoretical background and practical skills necessary to effectively apply the Six Sigma methodology in your business. It uses the powerful and proven concepts developed in statistical process control and provides you with the knowledge, tools and guidelines to apply them quickly and effectively within the DMAIC model. The focus is less on theory and more on how to apply these influential tools to conduct projects in your business. Understanding proper data collection methods and using the proper analysis tool from the myriad of possibilities will be emphasized. Data analysis techniques to uncover the root causes for process failure will be investigated, as well as how to implement lasting controls for sustained process improvement success.

You’ll learn to:

  • Differentiate between discrete and continuous data and how it affects your analysis
  • Use and apply Gage R&R to validate your measurement system
  • Select the proper analysis tool for a specific situation: Pareto charts, histograms, scatter plots, normality tests, ANOVA, correlation and regression analysis
  • Apply 2x2 Design of Experiments
  • Determine and undersand the effects of Cp , Cpk and other process capability metrics
  • Calculate sample size and use it in determining the scope of data collection activities
  • Validate and measure the effect of process improvements
  • Choose and apply the proper control tool: I-MR, Xbar, u-chart, p-chart
  • Apply Design of Experiment tutorial analysis to your improvement


  • Populations, samples and processes
  • Characteristics of the normal curve
  • Measuring center and variability of a process
  • Introduction to basic graphing techniques
  • Use of Pareto, histogram and scatter plots

Define Phase

  • Project prioritization using a simple decision matrix or QFD chart
  • Defining objectives in the context of the organization
  • Frame business problems as a closed-loop equation

Measure Phase

  • Discrete versus continuous data and the effect on analysis
  • Sample size and its effect on data collection
  • Using and applying Gage R&R
  • Data stratification techniques

Analyze Phase

  • Determining baseline process capability, Cp , Cpk
  • Selection of proper analysis tools
  • Normality testing and z scores
  • Hypothesis testing and tools, including ANOVA (Analysis of Variance)
  • Types of process errors

Improve Phase

  • Design of Experiments (DOE)—full and fractional
  • Validating and measuring the effects of process improvements
  • Regression analysis

Control Phase

  • Types of controls
  • Choosing and applying the proper controls, such as I-MR (individual and moving range), Xbar, p-charts and more
  • Techniques to sustain the gains achieved
  • Process monitoring
  • Developing standard operating procedures

Scott Converse

Scott Converse is the director of project management and process improvement programs for the Wisconsin School of Business. He has developed courses for and has expertise in the areas of project management, portfolio management, technology project implementation, process improvement, Six Sigma, business statistics, data analysis, and data mining.

Enroll: Six Sigma Data Measurement, Analysis, and Tools

Individual Sessions

5/18/2015 - 5/20/2015
$2195 Add to Cart