Statistical Strategies for Managers

Course Summary

This course is organized into bite-sized modules that can fit into a manager’s busy schedule, to acquaint management with the necessary thinking skills to understand how data, with the right statistical strategies, can be used to achieve specific business goals.

Event Details

16 hours

Instructor-led class training, with opportunities for questions and discussion. Games and demonstrations are used where possible to illustrate concepts.
Note: Managers are not taught how to use statistical software, but they are taught how to interpret the software output (either Minitab or JMP output). Customized Excel solutions tailored to Manager’s needs are provided for certain topics including selecting the right test and sample size.

Description

A weak link in the sustainability of training for engineers is the role of management to use statistical strategies, when appropriate, to direct data collection and require data analyses for decisions. Managers should have an understanding of statistical strategies for business objectives, managers should know what their engineers, researchers, and scientists are being taught, and managers should be able to leverage data to set the right priorities, direct the right actions, and make the right decisions. This course will acquaint managers with the statistical strategies and methods necessary to characterize, improve, and control processes and products.

This course is organized into bite-sized modules that can fit into a manager’s busy schedule, to acquaint management with the necessary thinking skills to understand how data, with the right statistical strategies, can be used to achieve specific business goals. Although managers are not taught here to use statistical software, they do learn what a method or strategy does, the correct assumptions for use, how to interpret software output, as well as how to check the validity of an analysis by knowing the right questions to ask and how to avoid common pitfalls. For example, managers are taught the important skill of knowing what sample size is required since this determines the time and resources necessary to reach a stated objective. By knowing the appropriate statistical strategy, managers can direct the right approach and have more confidence in decisions. With proper management engagement, a data-driven decision making culture can be instilled to improve business performance.

Who Should Attend

This course is designed for Managers who have engineers or researchers that are being taught statistical methods and who want to leverage their data for competitive advantage.

Learning Objectives

Through training, participants will:

  • Know the topics taught to engineers, researchers, and scientists, and understand what is possible with statistical strategies and concepts including: Statistical Thinking, SPC, MSA, Data Mining, DOE, and Qualification & Verification Tests
  • Become familiar with statistical methods and over 100 statistical strategies and when they should be used
  • Know definitions of statistical concepts and terms
  • Be able to interpret software output and know how to avoid common pitfalls
  • Know how to review a statistical analysis and know the right questions to ask and items to check
  • Over 80 self-test questions for Managers to practice the concepts

Course Outline

Module 1: Statistical Thinking (2 hours)

  • The Role of Management
  • The Data Difference and Why Use Statistics
  • Translation of an Engineering Problem to a Statistical Problem
  • How do you Know Something and how Statistics can Help
  • How Statistics can Give a Competitive Advantage
  • Statistical Application Opportunities for Business including the Major Application of How Engineers/Researchers Do their Daily Jobs
  • Manager Exercise: Which problems are statistical problems?
  • Principles of Statistical Thinking
  • Game: Testing Your Statistical Intuition
  • Risk Management through Diversification
  • Types of Variation—Common vs Special Cause
  • Common Cause Problem Solving Roadmap
  • Descriptive Statistics
    • Center: Mean vs Median
    • Spread: Range vs Stdev
      • Why Variances are Important
    • Shape: Normal and Skewed Distributions
      • Histograms, Probability Plots, Box Plots, Interval Plots
    • Graphical Pitfalls and How to Avoid Them
    • Manager Exercise: Questions to Ask when Reviewing Graphics.
    • Why center and spread are not a complete description.
  • What Managers Can Do
  • Summary of engineering or business objective, statistical technique or strategy, how to check validity and questions to ask.
  • Self Test: 11 Questions

 

Module 2: Control Methods (2 hours)

  • Control Method Selection
  • Why use SPC?
  • Where SPC is Applied
  • Data Type and Analysis Matrix
  • How Averages Behave
  • Xbar/R Charts, Individual/MR Charts
    • Interpretation of Minitab Output
    • What Controls the Width of the Control Limits.
  • 4 states of a Process
  • Naive Data Analysis
  • Manager Exercise: Critique on Management Action?
  • Process Set Up Optimal Strategy
  • Implementation Strategy
  • Identifying Key Process Input and Output Variables (KPIV’s, KPOV’s)
  • Selecting the Right Control Chart
  • Chart Design for Subgroup Size, Frequency, Subgrouping, OOC Rules
  • Control Chart Errors and What They Mean
  • How to Tell if a Chart is Effective
  • Out-of-Control-Action-Plan (OCAP)
  • How to Search for Assignable Cause
  • Four Problems with SPC, Special Topics and How to Fix Them
    • Batch Processing and the I/MR/R and I/R/I Charts
    • Non-Normality: Percentile Limits
    • Correlated Data: 3 Sigma Limits, EWMA
    • Fixed Effects: Individual Charts
    • Short Run SPC
    • Mixtures: Zero Inflated Distributions
    • Machines Running Different Products
    • Multivariate Control with Correlated Variables
  • Manager Exercise: Management use of SPC
  • Appendix: Ship-To-Control
  • Summary of engineering or business objective, statistical technique or strategy, how to check validity and questions to ask
  • Self Test: 10 Questions

Module 3: Measurement System Analysis (2 hours)

  • Why Measurement Studies are Important
  • Sources of Measurement Variation
  • Order to Collect Data
  • Game: Why Knowing Precision is Important
  • Measurement Terminology
    • Measurement Error
    • Bias, Accuracy and Precision
    • Assessing Resolution
    • Repeatability and Reproducibility
    • Bias and Linearity
    • Measurement Metrics: Percent of Tolerance; Percent of Variation; and How People Cheat on Gauge R&R
  • Gauge R&R
    • Interpretation of Minitab Output and What to Look For
    • Strategy to Reduce Repeatability by 30% or More with No Capital Investment
    • Expanded Gauge R&R
    • One-Sided Specs
    • Gauge Studies for Combined Repeatability and Reproducibility
    • Destructive and Non-Repeatable Testing
    • Special Methods to Reduce Nuisance Variation
    • Manager Exercise: Spotting Problems in a Variables Gauge R&R Study
    • Attribute Agreement Analysis
      • Percent Agreement
      • Kappa Statistics
      • Overkill and Escape Assessment
      • How People Cheat on Attribute MSA Studies
  • Bias and Linearity Studies
    • Type I Gauge Studies
    • Bias and Linearity Tests and Calibration Curves
    • Engineering Delta Test
  • Tool Matching
    • Correlation Criteria
    • Orthogonal Regression Criteria
    • Paired t-Test
  • Summary of engineering or business objective, statistical technique or strategy, how to check validity and questions to ask.
  • Self Test: 12 Questions

Module 4: Decision Making with Data (4 hours)

  • Problems with Observational Data
  • Data Cleaning
  • Statistical Reasoning
    • Game: ESP Testing
    • 4 Steps in a Statistical Test
    • 2 Decision Errors
    • 2 Ways to reduce Missed Signals
    • Statistical Significance: p-Values
    • Practical Significance
    • 5 Conditions to Accept a Conclusion from Data
    • Confidence Intervals for Managers
  • One, Two and Three or More Sample Comparisons
    • t-Test
    • Paired t-Test
    • Game: Testing Golf Putting Performance
    • ANOVA
    • Applications
    • Multifactor ANOVA
  • Modeling Continuous Inputs vs Continuous Outputs
    • Simple Linear Regression Analysis
    • Multiple Regression
  • Modeling Discrete Inputs on a Discrete Output
    • Chi Square Analysis
  • Modeling Continuous Inputs on a Continuous Output
    • Logistic Regression
  • Multivariate Methods
    • PCA
    • PLS
    • Discriminant Analysis
    • Recursive Partitioning
  • Summary of engineering or business objective, statistical technique or strategy, how to check validity and questions to ask.
  • Self Test: 18 Questions

Module 5: Planning, Executing and Analyzing Evaluations (Design of Experiments) (4 hours)

  • Brief History of DOE
  • Terminology for DOE
  • 6 Purposes for DOE
  • Delusion of Experiment vs Design of Experiments
  • One-Factor-at-a-Time Experimentation
  • Main Effects
  • Interaction
  • Principles of DOE
  • Replication
  • Randomization
  • Importance of DOE
  • One Factor Experiments
    • Simple Comparative Experiments
    • Comparing Proportions
    • One Continuous Factor Optimization
    • Multi Factor Experiments
    • General Full Factorials
    • Two Level Factorials
    • Blocking
    • Unreplicated Full Factorials
    • Idea of Robustness
    • Center points
    • Actions from a DOE
    • Fractional Factorials
  • Optimization Strategy
    • CCD and Box Behnken Designs for Optimization
    • Multiple Response Optimization with Desirability Functions
    • Confirmation Runs
  • Planning a DOE
  • Game: Optimizing a Process with Noise
  • What can go Wrong in a DOE and Special Situations
    • Hard-to-Vary Factors
    • Experiments with Factors at Multiple Process Steps
    • Botched Runs
    • D and I Optimal Designs
    • Covariates
    • Robust Design
    • Ruggedness Testing
    • EVOP
    • Mixture Designs
  • Manager Exercises
  • Appendices:
  • Troubleshooting Guide for Non-Significant DOE’s
  • 60 Ways to Mess Up a DOE
  • Summary of engineering or business objective, statistical technique or strategy, how to check validity and questions to ask.
  • Self Test: 20 Questions

Module 6: Qualification and Verification Methods (2 hours)

  • Benefits of Knowing Statistical Methods for Qualification and Verification Testing
  • Two Important Considerations in Qualification and Verification Testing
  • Verification, Validation and Qualification Distinctions
  • Translating Technical Requirements into Statistical Requirements
  • Selecting a Statistical Test
  • Concepts of Sample Size and Risk
  • Number of Lots for Testing
  • Review of Statistical Testing
    • Steps, Decision Errors, p-Values, 5 Conditions to Accept a Conclusion
  • Tests Comparing Groups for Expected Performance
    • Comparing Means with t-Tests
    • Comparing Means with the Equivalence Test
    • Comparing Means with Different Test Conditions (Blocks)
    • Comparing Percent Conformance (Yield)
    • Minimal Sample Size Tests: Sequential Analysis Methods
      • History, Concept and Definition
      • When Sequential Analysis is Appropriate
        • Testing Means
        • Testing Proportions
        • Game: Sequential Analysis
      • Comparing Variation for Two or More Variances
      • Multiple Test Comparisons
    • Comparing Metal Levels to Medians using Binomial
      • Censored Data Methods
        • Partially Censored Data Comparison in Minitab
        • Totally Censored Data Comparison using Fisher’s Exact Test
    • Change FMEA
  • Manager Exercise: Recommend a test and sample size for the following situations
  • Summary of engineering or business objective, statistical technique or strategy, how to check validity and questions to ask.
  • Self Test: 10 Questions

Prerequisites

None

Instructors

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