Statistical Thinking Improving Business Performance

Apply statistics in business to achieve performance improvement

Statistical Thinking: Improving Business Performance, 3rd Edition helps managers understand the role of statistics in implementing business improvements. It guides professionals who are learning statistics in order to improve performance in business and industry. It also helps graduate and undergraduate students understand the strategic value of data and statistics in arriving at real business solutions. Instruction in the book is based on principles of effective learning, established by educational and behavioral research.

The authors cover both practical examples and underlying theory, both the big picture and necessary details. Readers gain a conceptual understanding and the ability to perform actionable analyses. They are introduced to data skills to improve business processes, including collecting the appropriate data, identifying existing data limitations, and analyzing data graphically. The authors also provide an in-depth look at JMP software, including its purpose, capabilities, and techniques for use.

Updates to this edition include:

• A new chapter on data, assessing data pedigree (quality), and acquisition tools
• Discussion of the relationship between statistical thinking and data science
• Explanation of the proper role and interpretation of p-values (understanding of the dangers of “p-hacking”)
• Differentiation between practical and statistical significance
• Introduction of the emerging discipline of statistical engineering
• Explanation of the proper role of subject matter theory in order to identify causal relationships
• A holistic framework for variation that includes outliers, in addition to systematic and random variation
• Revised chapters based on significant teaching experience
• Content enhancements based on student input

This book helps readers understand the role of statistics in business before they embark on learning statistical techniques.

DR. ROGER W. HOERL is an associate professor at Union College where he teaches statistics, engineering statistics, design of experiments, regression analysis, and big data analytics. Previously, he led the Applied Statistics Laboratory at GE Global Research.

DR. RONALD D. SNEE is founder and president of Snee Associates, an authority on designing and implementing organizational improvement and cost-reduction solutions. Prior to this role, he worked at the DuPont Company in a variety of assignments. Snee has co-authored five books and published more than 330 articles on process improvement, quality, and statistics.

Preface

Introduction to JMP

Part One: Statistical Thinking Concepts

1. Need for Business Improvement

a. Today’s Business Realities and the Need to Improve

b. We now Have Two Jobs: A model for Business Improvement

c. New Management Approaches Require Statistical Thinking

d. Principles of Statistical Thinking

e. Applications of Statistical Thinking

f. Summary and Looking Forward

g. Notes

2. Data: the Missing Link

a. Why Do We Need Data?

b. Types of Data

c. All Data Are Not Created Equal

d. Practical Sampling Tips to Ensure Data Quality

e. What about Data Quantity?

f. Documenting the Data Pedigree – The Data’s Story

g. The Measurement System

h. Summarizing Data

i. Summary and Looking Forward

j. Notes

3. Statistical Thinking Strategy

a. Case Study: The Effect of Advertising on Sales

b. Case Study: Improvement of a Soccer Team’s Performance

c. Statistical Thinking Strategy

d. Variation in Business Processes

e. Synergy between Data and Subject Matter Knowledge

f. Dynamic Nature of Business Processes

g. Value of Graphics - Discovering the Unexpected

h. Summary and Looking Forward

i. Project Update

j. Notes

4. Understanding Business Processes

a. Examples of Business Processes

b. SIPOC Model for Processes

c. Identifying Business Processes

d. Analysis of Business Processes

e. Process Complexity

f. The Hidden Plant – Another Source of Waste and Complexity

g. Process Measurements

h. Benchmarking

i. Systems of Processes

j. Summary and Looking Forward

k. Project Update

l. Notes

Part Two: Holistic Improvement: Frameworks and Basic Tools

5. Holistic Improvement: Tactics to Deploy Statistical Thinking

a. Case Study: Revolving Customer Complaints of Baby Wipe Flushability

b. The Problem-Solving Framework

c. Case Study: Reducing Resin Output Variation

d. The Process Improvement Framework

e. Statistical Engineering

f. Statistical Engineering Case Study: Predicting Corporate Defaults

g. A Framework for Statistical Engineering Projects

h. Summary and Looking Forward

i. Project Update

j. Notes

6. Process Improvement and Problem-Solving Tools

a. Practical Tools

b. Knowledge-Based Tools

c. Graphical Tools

d. Analytical Tools

e. Summary and Looking Forward

f. Project Update

g. Notes

Part Three: Formal Statistical Methods

7. Building and Using Models

a. Examples of Business Models

b. Types of Models

c. Regression Modeling Process

d. Building Models with One Predictor Variable

e. Building Models with Several Predictor Variables

f. Multicollinearity: Another Model Check

g. Some Limitations of Using Observational Data

h. Summary and Looking Forward

i. Project Update

j. Notes

8. Using Process Experimentation to Build Models

a. Randomized versus Observational Studies

b. Why Do We Need a Statistical approach?

c. Examples of Process Experiments

d. Statistical Approach to Experimentation

e. Two Factor Experiments: A Case Study

f. Three Factor Experiments: A Case Study

g. Larger Experiments

h. Blocking, Randomization and Center Points

i. Summary and Looking Forward

j. Project Update

k. Notes

9. Applications of Statistical Inference Tools

a. Examples of Statistical Inference Tools

b. Process of Applying Statistical Inference

c. Statistical Confidence and Prediction Intervals

d. Statistical Hypothesis Tests

e. Sample Size Formulas

f. Summary and Looking Forward

g. Project Update

h. Notes

10. Underlying Theory of Statistical Inference

a. Applications of the Theory

b. Theoretical Framework of Statistical Inference

c. Probability Distributions

d. Sampling Distributions

e. Linear Combinations

f. Transformations

g. Summary and Looking Forward

h. Project Update

i. Notes

Appendix A Effective Teamwork

Appendix B Presentations and Report Writing

Appendix C More on Surveys

Appendix D More on Regression

Appendix E More on Design of Experiments

Appendix F More on Inference Tools

Appendix G More on Probability Distributions

Appendix H DMAIC Process Improvement Framework

Appendix I T Critical Values

Appendix J Standard Normal Probabilities (Cumulative Z Curve Areas)

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