Programs

Experimental Practices

Program Description

The Experimental Practices program provides the participant with the fundamental tools and procedures required to function in the world of statistical experimental design. This program includes all prerequisite courses that are needed to fully comprehend and capitalize on the training of experimental design, commonly referred to as Design of Experiments or simply DOE. This program will arm the participant with the insights necessary to plan, execute, analyze, interpret and report the results of statistically designed experiments as well as how the application of a statistically designed experiment can be used to establish the optimum operating conditions for one or more input variables, also called adjustment factors.

Participants will learn how to translate a practical problem into a statistical problem and then isolate simple and complex cause-and-effect relationships which often remain undetected with traditional problem-solving methods. Students will learn how to depict and communicate the results of a statistics-based experiment in down-to-earth language. Of special interest, the instructional content even provides the participant with the helpful insights, short-cuts and tips on how to establish a post-experiment action plan. A heavy emphasis is placed on the application of fractional factorial experiments. Of course, such experiments are intended to discover the vital few variables as contrasted to the trivial many. In short, fractional factorial experiments can provide a screen for filtering potentially causal variables, thereby allowing the practitioner to discover improvement leverage.

From this frame of reference, the participant will also learn how to use fractional factorial experiments as an economy measure when the availability of samples or the cost of sampling restricts the use of a full factorial experiment. In addition, a primary focus is placed on the key design principles, primary methods of data analysis, and powerful graphical procedures that drives success. From here, the participant is fully prepared to move on to more advanced experimental methods and statistical procedures.

Reinforcement of major concepts, techniques, and applications is realized through exercises, scenarios, case studies, and field studies. Through this training, the participant will gain tremendous insight into the field of statistically designed experiments as well as the logic and reasoning which underlies Six Sigma and business process improvement. 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

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

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

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

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

Experimental Methods

Design Principles - Understand the principles of experiment design and analysis

Design Models - Describe the various types of designed experiments and their applications

Experimental Strategies - Outline a strategy for designing and analyzing a statistical experiment

Experimental Effects - Define the various types of experimental effects and how they impact decisions

One-Factor Two Level - Configure and analyze a one-factor two-level statistically based experiment

One-Factor Multi Level - Configure and analyze a one-factor multi-level statistically based experiment

Full Factorials - Understand the nature and underlying logic of full factorial experiments

Two-Factor Two Levels - Configure and analyze a two-factor two-level statistically based experiment

Instruction Videos
1. Two-Factor Two-Level Experiment Design - Part A -  4m 44s - 4.47 MB
2. Two-Factor Two-Level Experiment Design - Part B -  3m 29s - 3.29 MB
3. Two-Factor Two-Level Experiment Design - Part C -  2m 54s - 2.75 MB
4. Calculation and Display of Key Experimental Effects -  5m 04s - 4.78 MB
5. Graphing and Interpretation of Main Effects -  4m 33s - 4.30 MB
6. Graphing and Interpretation of Interactions - Part A -  4m 58s - 4.71 MB
7. Graphing and Interpretation of Interactions - Part B -  4m 46s - 4.50 MB
8. Creation and Structure of a Two-Way ANOVA Table -  8m 22s - 7.94 MB
9. Discovering the Path-of-Steepest-Assent - Part A -  8m 13s - 7.78 MB
10. Discovering the Path-of-Steepest-Assent - Part B -  5m 43s - 5.41 MB
Expansion Videos
11. Use of DOE to Facilitate Team Decision Making - Part A -  3m 26s - 1.39 MB
12. Use of DOE to Facilitate Team Decision Making - Part B -  6m 00s - 4.68 MB
13. Use of DOE to Facilitate Team Decision Making - Part C -  3m 28s - 1.54 MB
14. Use of DOE to Facilitate Team Decision Making - Part D -  5m 04s - 2.88 MB
15. Use of DOE to Facilitate Team Decision Making - Part E -  4m 24s - 2.30 MB
16. Use of DOE to Facilitate Team Decision Making - Part F -  3m 22s - 2.30 MB
17. Use of DOE to Facilitate Team Decision Making - Part G -  6m 01s - 2.99 MB
18. Use of DOE to Facilitate Team Decision Making - Part H -  2m 39s - 1.29 MB
Application Videos
19. Contrast Method for Conducting an ANOVA - Part A -  9m 33s - 10.82 MB
20. Contrast Method for Conducting an ANOVA - Part B -  2m 55s - 3.62 MB
21. Generation of a Two-Factor Two-Level Design - Part A -  6m 17s - 6.92 MB
22. Generation of a Two-Factor Two-Level Design - Part B -  2m 32s - 2.88 MB
23. Analysis of a Replicated Two-Factor Experiment -  7m 48s - 8.90 MB
24. Analysis of an Unreplicated Two-Factor Experiment -  5m 31s - 5.42 MB
25. Catapult Case Study for a Two-Factor Experiment -  11m 40s - 8.88 MB
Supporting Media
Summary Slides: Two-Factor Two Levels

Two-Factor Multi Level - Configure and analyze a two-factor multi-level statistically based experiment

Three-Factor Two Level - Configure and analyze a three-factor two-level statistically based experiment

Planning Experiments - Understand the planning and implementation considerations related to statistical experiments

Fractional Factorials - Understand the nature and underlying logic of fractional factorial experiments

Four-Factor Half-Fraction - Configure and analyze a four-factor half-fraction statistically based experiment

Five-Factor Half-Fraction - Configure and analyze a five-factor half-fraction statistically based experiment

Screening Designs - Understand how to select, implement, and analyze a screening experiment

Robust Designs - Explain the purpose of robust design and define several practical usages

Experiment Simulation - Describe how a DOE can be employed when measurement data is not available

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