### +Process Documentation and Analysis in Six Sigma

**Overview/Description**

During the Define stage of a Six Sigma project, the Six Sigma team identifies process areas that require analysis and improvement. During the Measure stage, the team maps the processes and procedures that were identified. As this mapping takes place, the team begins to uncover likely causes of the problems and analyze them fully. This course will examine key tools and techniques used to model and analyze existing processes. From a process modeling perspective, the course looks at techniques such as process mapping, written procedures, and work instructions. This course is aligned to the ASQ Body of Knowledge and is designed to assist Green Belt candidates toward achieving their certifications and becoming productive members of their Six Sigma project teams.

**Target Audience**

Candidates seeking Six Sigma Green Belt certification; quality professionals, engineers, production managers, and frontline supervisors; process owners and champions charged with the responsibility of improving quality and processes at the organizational or departmental level

**Process Documentation and Analysis in Six Sigma**

- recognize the categories of information obtained through process modeling
- identify the information to look for when analyzing a process model
- recognize the role of process modeling in the Measure phase of a Six Sigma project
- match the process map types to the situations in which they best apply
- label process map symbols according to their meanings
- recognize the activities involved in creating a process map
- interpret a given process map
- recommend improvements to a process given a process map
- identify elements of a procedure document
- recognize characteristics of work instructions
- recognize how work instructions differ from written procedures

### +Basic Probability and Statistical Distributions in Six Sigma

**Overview/Description**

To make accurate inferences about populations from sample data, you need to be able to determine the probability that an event or a combination of events will occur. You also need to be familiar with the characteristics of various statistical distributions, and their suitability for different types of data. In this course, you’ll be introduced to the concept of probability. You’ll learn how to calculate probability involving independent events, mutually exclusive events, multiplication rules, permutations, and combinations. You’ll also look at different types of distributions, such as normal, Poisson, binomial, Chi-square, Student’s t, and F-distributions. This course is aligned to the ASQ Body of Knowledge and is designed to assist Green Belt candidates toward achieving their certifications and becoming productive members of their Six Sigma project teams.

**Target Audience**

Candidates seeking Six Sigma Green Belt certification; quality professionals, engineers, production managers, and frontline supervisors; process owners and champions charged with the responsibility of improving quality and processes at the organizational or departmental level

**Basic Probability and Statistical Distributions in Six Sigma**

- match the terms associated with probability to their definitions
- use the formulas for calculating the probability of simple and mutually exclusive events
- calculate the probability of given events
- calculate the probability of given events
- calculate probability using the addition rule
- calculate probability using the multiplication rule
- use addition and multiplication rules to calculate the probability of given events
- calculate a permutation
- calculate a combination
- use formulas to calculate permutations and combinations
- label examples of variables as continuous or discrete
- identify characteristics of normal distribution
- calculate probabilities based on normal distribution
- use formulas to calculate probability
- recognize examples of results that you could summarize using a binomial distribution
- recognize examples of results that you could summarize using a Poisson distribution
- distinguish between uses of binomial and Poisson distributions
- recognize characteristics of chi-square distributions
- identify uses for Student’s t-distributions
- identify characteristics of F-distributions
- distinguish between Chi-square, Student’s t, and F-distributions

### +Data Classification and Collection in Six Sigma

**Overview/Description**

Before a Six Sigma team can begin to improve an organization’s processes, it must measure key performance indicators. In doing so, the team identifies, collects, and analyzes data related to the processes. This course introduces basic types of data, such as continuous and discrete data, as well as various measurement scales. You will learn how to plan data collection and how to use data sampling techniques and data collection tools, such as check sheets. This course is aligned to the ASQ Body of Knowledge and is designed to assist Green Belt candidates toward achieving their certification and becoming productive members of their Six Sigma project teams.

**Target Audience**

Candidates seeking Six Sigma Green Belt certification; quality professionals, engineers, production managers, and frontline supervisors; process owners and champions charged with the responsibility of improving quality and processes at the organizational or departmental level

**Data Classification, Sampling, and Collection in Six Sigma**

- distinguish between examples of qualitative and quantitative data
- label examples as either discrete or continuous data
- determine whether to use discrete or continuous data, given a scenario
- determine the type of data to gather, given a scenario
- determine the type of measurement scale being used, given an example
- demonstrate your understanding about the four levels of measurement
- identify principles of data sampling
- match sampling methods with corresponding characteristics
- identify situations when you would use simple random sampling
- identify situations when you would use stratified sampling
- determine the best data sampling method, given a scenario
- identify characteristics of automated data collection
- identify data collection best practices
- recognize characteristics of technologies used for data collection
- identify key considerations in creating a data collection plan
- make data collection decisions, given a scenario
- match types of check sheets with examples of when each type would be used
- recognize examples of dating coding methods
- recognize appropriate use of data collection strategies, given a scenario

### +Statistics and Graphical Presentation in Six Sigma

**Overview/Description**

Basic graphs and tables can be used to summarize and assess performance-related data in a meaningful way. Six Sigma practitioners use descriptive statistics to tabulate and graphically represent sample data through a number of informative charts and diagrams. Using analytical statistics, inferences are made about the larger population based on their sample data. These tools allow the organization to view its performance graphically and draw valid conclusions about its processes and products. This course provides basic statistical tools for describing, presenting, and analyzing data. It explores the process of preparing and presenting sample data using graphical methods and then making valid inferences about the population represented by the sample. This course is aligned to the ASQ Body of Knowledge and is designed to assist Green Belt candidates toward their certification and to become productive members on their Six Sigma project teams.

**Target Audience**

Candidates seeking Six Sigma Green Belt certification; also quality professionals, engineers, production managers, and frontline supervisors; process owners and champions charged with the responsibility of improving quality and processes at the organizational or departmental level

**Statistics and Graphical Presentation in Six Sigma**

- distinguish between the characteristics of descriptive and inferential statistics
- recognize the implications of the central limit theorem for inferential statistics
- match tools for inferential statistics to descriptions of their use
- demonstrate your understanding of concepts related to inferential statistics
- calculate measures of central tendency
- calculate measures of dispersion
- use measures of central tendency and dispersion, given a scenario
- interpret a given frequency distribution table
- calculate cumulative frequency distribution, given a dataset
- calculate and use frequency distribution information on a Six Sigma project
- match scatter diagrams with corresponding interpretations
- interpret a given probability plot
- recognize attributes of a process given a histogram
- interpret a given stem-and-leaf plot
- interpret a given box-and-whisker plot
- interpret given graphical presentations

### +Measurement System Analysis in Six Sigma

**Overview/Description**

The adequacy and accuracy of measurement systems is vital to the success of any data collection and analysis, and therefore critical to the overall success of any Six Sigma initiatives in an organization. Measurement systems encompass devices, procedures, and the human element of measurement, which together must produce correct measurements and comply with appropriate standards. This course examines how to analyze a measurement system to help it produce correct measurements and minimize its proportion of variability in the overall process variability. It introduces key elements of measurement system capability using gauge repeatability and reproducibility (GR&R) studies, measurement correlation, bias, linearity, percent agreement, and precision to tolerance (P/T) ratio. This course is aligned to the ASQ Body of Knowledge and is designed to assist Green Belt candidates toward their certification and become productive members on their Six Sigma project teams.

**Target Audience**

Candidates seeking Six Sigma Green Belt certification; quality professionals, engineers, production managers, and frontline supervisors; and process owners and champions charged with the responsibility of improving quality and processes at the organizational or departmental level

**Measurement System Analysis in Six Sigma**

- distinguish between accuracy and precision
- identify types of correlation
- demonstrate your understanding of concepts related to measurement system analysis
- match types of variation with corresponding characteristics
- calculate the gauge repeatability and reproducibility value for a given set of data
- interpret data from a given gauge repeatability and reproducibility study
- calculate a precision to tolerance ratio
- conduct a gauge repeatability and reproducibility study and calculate the P/T ratio, given a scenario
- identify best practices in conducting a bias study
- calculate and interpret bias, in a given scenario
- conduct a bias study
- identify attributes of linearity
- calculate linearity and interpret a linearity plot
- conduct a linearity study
- recognize situations when you would use percent agreement to assess a measurement system
- interpret numeric output from a given percent agreement analysis
- recommend changes to a measurement system based on percent agreement findings

### +Process and Performance Capability Measurement in Six Sigma

**Overview/Description**

Organizations should regularly evaluate existing processes to make sure they meet targets and specifications set by their customer and business requirements. Measuring and analyzing process capability and performance enables an organization to report its sigma level and improvement teams to targets their efforts effectively. This course explores the key concepts related to process capability and performance, and the methods of measuring and interpreting common performance indices. It also discusses how to verify the stability and normality of a given process and identify key considerations for measuring process capability, such as short-term and long-term capability and sigma shift.This course is aligned to the ASQ Body of Knowledge and is designed to assist Green Belt candidates toward their certification and become productive members on their Six Sigma project teams.

**Target Audience**

Candidates seeking Six Sigma Green Belt certification; also quality professionals, engineers, production managers, and frontline supervisors; process owners and champions charged with the responsibility of improving quality and processes at the organizational or departmental level

**Process and Performance Capability Measurement in Six Sigma**

- distinguish between process limits and specification limits
- categorize examples of performance metrics
- demonstrate your understanding of concepts related to process performance and capability
- sequence the steps of a process capability study
- determine the stability of a process using a control chart
- identify methods of verifying normality for a given process
- determine the stability and normality of a given process
- calculate the Cp of a given process
- calculate the Cpk of a given process
- rate the capability of a process based on Cp and Cpk values
- calculate the Pp of a given process
- calculate the Ppk of a given process
- identify the characteristics of the Cpm index and when it’s used
- assess the capability and performance of a given process
- interpret the capability and performance index results in a process capability study
- recommend action to improve a process in a given scenario
- estimate the long-term capability of a given process
- assess a given process using capability and performance indices