Programs

Diagnostic Practices

Program Description

The Diagnostic Practices program will endow the participant with the knowledge and insights necessary to judiciously plan and successfully execute a diagnostic study. Participants will learn how to fully characterize the statistical performance of a process and identify the dominant families of variation. In many instances, the simple application of a few diagnostic tools can often preclude the need for exhaustive experimentation. Of course, such an action has the potential to shorten the total time it takes to execute an improvement project.

Students will discover a selected array of powerful analytical and statistical tools that are essential for isolating critical sources of variation related to process centering and spread. Major emphasis is given to the methods and techniques for statistically analyzing, describing, and displaying performance data – for virtually all types of products, processes, services and transactions. In particular, the participant will learn how to select the right variables and parameters for inclusion in a factorial experiment. Participants will learn how to establish operating tolerances for almost any type of product, process or service. Of special interest, the participant will learn the theory and application of common sampling methods as well as how to draw valid conclusions and make statistical inferences from a sampling distribution. In support of this, the participant will also learn how to draw such conclusions with known degrees of statistical risk and confidence.

Of course, the critical tools and concepts associated with statistical hypothesis testing is thoroughly discussed and then related to the use of diagnostic tools, design-of-experiments, and statistical process control methods. Related to this instructional goal, the participant will also be taught how to construct statistical hypotheses and then how to test those hypotheses using well established methods, such as the common t-test, analysis-of-variance, and regression, just to mention a few. However, when the assumptions underlying the use of parametric tools can not be reasonably satisfied, the practitioner sometimes finds it necessary to employ nonparametric methods, or so called distribution free methods. To this end, the participant will learn how to employ such tools as the median test (and sign test) to evaluate a relatively diverse range of statistical hypotheses.

The knowledge gained from this curriculum is paramount to the effective use of performance metrics and indices of process capability. Reinforcement of the major techniques and applications is realized through exercises, scenarios, and case studies. Total instructional time for this program is approximately 60 hours.


      Printable Program Outline

Training Orientation

Excel Orientation - Explore the Excel software package

Minitab Orientation - Explore the Minitab software package

Simulator Orientation - Explore the Process Simulator

Breakthrough Vision

Deterministic Reasoning - Describe a basic cause-and-effect relationship in terms of Y=f(X)

Leverage Principle - Relate the principle of leverage to an improvement project

Process Management

Performance Yield - Explain why final yield is often higher than first-time yield

Hidden Processes - Describe the non-value added component of a process

Measurement Power - Describe the role of measurement in an improvement initiative

Establishing Baselines - Explain why performance baselines are essential to realizing improvement

Defect Opportunity - Understand the nature of a defect opportunity and its role in metrics reporting

Process Models - Define the key features of a Six Sigma performance model

Process Capability - Identify the primary indices of process capability

Design Complexity - Describe the impact of complexity on product and service quality

Quality Tools

Variable Classifications - Define the various types of variables commonly encountered during quality improvement

Measurement Scales - Describe each of the four primary scales of measure and their relative power

Problem Definition - Characterize the nature of a sound problem statement

Focused Brainstorming - Explain how focused brainstorming is used to facilitate improvement efforts

Process Mapping - Understand how to define the flow of a process and map its operations

SIPOC Diagram - Describe the nature and purpose of an SIPOC diagram

Force-Field Analysis - Utilize force field analysis to solve problems

Matrix Analysis - Understand how matrices are created and used to facilitate problem solving

C&E Analysis - Explain how C&E matrices can be used to solve quality problems

Failure Mode Analysis - Understand how FMEA is used to realize process and design improvements

Performance Sampling - Explain how to design and implement a sampling plan

Check Sheets - Understand how check sheets can be used for purposes of data collection

Analytical Charts - Identify the general range of analytical charts that can be used to assess performance

Pareto Charts - Explain how Pareto charts can be used to isolate improvement leverage

Run Charts - Utilize run charts to assess and characterize time-based process data

Multi-Vari Charts - Define the major families of variation and how they can be graphed

Correlation Charts - Utilize a correlation chart to illustrate the association between two variables

Frequency Tables - Explain how to construct and interpret a frequency table

Performance Histograms - Construct and interpret a histogram and describe several purposes

Basic Probability - Understand basic probability theory and how it relates to process improvement

Pre-Control Charts - Describe the fundamental rules that guide the operation of a standard pre-control plan

Control Charts - Explain the purpose of statistical process control charts and the logic of their operation

Score Cards - Understand the purpose of Six Sigma score cards and how they are deployed

Search Patterns - Explain how the use of designed experiments can facilitate problem solving

Concept Integration - Understand how to sequence a given selection of quality tools to better solve problems

Quality Simulation - Employ the related quality tools to analyze data generated by the process simulator

Basic Statistics

Performance Variables - Identify and describe the types of variables typically encountered in field work

Statistical Notation - Recognize and interpret the conventional forms of statistical notation

Performance Variation - Explain the basic nature of variation and how it can adversely impact quality

Normal Distribution - Describe the features and properties that are characteristic of a normal distribution

Distribution Analysis - Explain how to test the assumption that a set of data is normally distributed

Location Indices - Identify, compute, and interpret the mean, median, and mode

Dispersion Indices - Identify, compute, and interpret the range, variance, and standard deviation

Quadratic Deviations - Understand the nature of a quadratic deviation and its basic purpose

Variation Coefficient - Compute and interpret the coefficient of variation

Deviation Freedom - Explain the concept of degrees-of-freedom and how it is used in statistical work

Standard Transform - Describe how to transform a set of raw data into standard normal deviates

Standard Z-Probability - Describe how to convert a standard normal deviate into its corresponding probability

Central Limit - Understand that the distribution of sampling averages follows a normal distribution

Standard Error - Recognize that the dispersion of sampling averages is described by the standard error

Student's Distribution - Understand that the T distribution applies when sampling is less than infinite

Standard T-Probability - Describe how to convert a T value into its corresponding probability

Statistics Simulation - Employ basic statistics to analyze data generated by the process simulator

Hypothesis Testing

Statistical Inferences - Explain the concept of a statistical inference and its primary benefits

Statistical Questions - Explain the nature and purpose of a statistical question

Statistical Problems - Understand why practical problems must be translated into statistical problems

Null Hypotheses - Define the nature and role of null hypotheses when making process improvements

Alternate Hypotheses - Define the nature and role of alternate hypotheses when making process improvements

Statistical Significance - Explain the concept of statistical significance versus practical significance

Alpha Risk - Explain the concept of alpha risk in terms of the alternate hypothesis

Beta Risk - Define the meaning of beta risk and how it relates to test sensitivity

Criterion Differences - Explain the role of a criterion difference when testing hypotheses

Decision Scenarios - Develop a scenario that exemplifies the use of hypothesis testing

Sample Size - Define the statistical elements that must be considered when computing sample size

Instruction Videos
1. Nature and Implications of Sample Size - Part A -  3m 50s - 3.89 MB
2. Nature and Implications of Sample Size - Part B -  3m 58s - 4.06 MB
3. Determination of Sample Size for Experiments - Part A -  7m 00s - 7.12 MB
4. Determination of Sample Size for Experiments - Part B -  8m 13s - 8.35 MB
5. Key Considerations for Computing Sample Size -  9m 41s - 9.85 MB
6. Calculation of an Appropriate Sample Size - Part A -  5m 14s - 5.32 MB
7. Calculation of an Appropriate Sample Size - Part B -  3m 15s - 3.32 MB
8. Calculation of an Appropriate Sample Size - Part C -  5m 26s - 5.51 MB
9. Calculation of an Appropriate Sample Size - Part D -  7m 42s - 7.85 MB
10. Calculation of an Appropriate Sample Size - Part E -  6m 12s - 6.29 MB
11. Relationship between Six Sigma and Sample Size -  9m 29s - 9.64 MB
12. Role of Sample Size in Hypothesis Testing - Part A -  5m 46s - 5.85 MB
13. Role of Sample Size in Hypothesis Testing - Part B -  7m 41s - 7.81 MB
Application Videos
14. Example Calculation of Sample Size - Part A -  6m 34s - 3.04 MB
15. Example Calculation of Sample Size - Part B -  3m 38s - 1.84 MB
16. Calculation of Sample Size for Discrete Data - Part A -  3m 57s - 4.10 MB
17. Calculation of Sample Size for Discrete Data - Part B -  2m 49s - 2.81 MB
18. Power of a Test and Random Sampling - Part A -  4m 48s - 4.35 MB
19. Power of a Test and Random Sampling - Part B -  1m 41s - 1.90 MB
20. Power of a Test and Random Sampling - Part C -  3m 03s - 3.59 MB
Supporting Media
Summary Slides: Sample Size

Confidence Intervals

Mean Distribution - Comprehend and characterize the distribution of sampling averages

Mean Interval - Compute and interpret the confidence interval of a mean

Variance Distribution - Comprehend and characterize the distribution of sampling variances

Variance Interval - Compute and interpret the confidence interval of a variance

Proportion Distribution - Comprehend and characterize the distribution of sampling proportions

Proportion Interval - Compute and interpret the confidence interval of a proportion

Frequency Interval - Describe how frequency of defects is related to confidence intervals

Parametric Methods

Mean Differences - Determine if two means are statistically different from each other

Variance Differences - Determine if two variances are statistically different from each other

Variation Total - Compute and interpret the total sums-of-squares

Variation Within - Compute and interpret the within-group sums-of-squares

Variation Between - Compute and interpret the between-group sums-of-squares

Variation Analysis - Explain how the analysis of variances can reveal mean differences

One-Way ANOVA - Construct and interpret a one-way analysis-of-variance table

Two-Way ANOVA - Construct and interpret a two-way analysis-of-variance table

N-Way ANOVA - Construct and interpret an N-way analysis-of-variance table

ANOVA Graphs - Construct and interpret a main effects plot as well as an interaction plot

Linear Regression - Conduct a linear regression and construct an appropriate model

Multiple Regression - Conduct a multiple regression and construct an appropriate model

Residual Analysis - Compute and analyze the residuals resulting from a simple regression

Parametric Simulation - Apply general regression methods to the process simulator

Chi-Square Methods

Statistical Definition - Describe how to translate a practical problem into a statistical problem

Model Fitting - Explain what is meant by the term "Model Fitting" and discuss its practical role in Six Sigma work

Testing Independence - Explain how a test of independence can be related to the idea of correlation

Contingency Coefficients - Understand how a contingency coefficient relates to a cross-tabulation table

Yates Correction - Describe the role of Yates correction in terms of the chi-square statistic

Testing Proportions - Test the significance of two proportions using the Chi-square statistic

Survey Methods

Research Design - Explain how the idea of research design fit with the idea of problem Solving

Information Sources - Explain how the idea of research design fit with the idea of problem Solving

Questionnaire Construction - Describe the role of survey demographics when analyzing closed-form survey data

Formulating Questions - Identify several things that should be avoided when developing survey questions

Question Quality - Explain what is meant by the term "question quality" and how this idea relates to data analysis

Sampling Plans - Describe several different types of sampling plans commonly used in survey research

Data Analysis - Explain how categorical survey data can be analyzed to establish strength of association

Nonparametric Methods

Nonparametric Concepts - Explain the difference between parametric and nonparametric methods

Median Test - Execute a median test on two groups and then determine if the difference is statistically significant

Runs Test - Conduct a runs test to determine if a time series pattern is random

Other Tests - Identify two nonparametric methods other than a median or runs test

Measurement Analysis

Measurement Uncertainty - Understand the concept of measurement uncertainty

Measurement Components - Describe the components of measurement error and their consequential impact

Measurement Studies - Explain how a measurement systems analysis is designed and conducted