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.
Table of Contents:
Preface xiii
Introduction to JMP xvii
Part One Statistical Thinking Concepts 1
Chapter 1 Need for Business Improvement 3
Today’s Business Realities and the Need to Improve 4
We Now Have Two Jobs: A Model for Business Improvement 8
New Improvement Approaches Require Statistical Thinking 12
Principles of Statistical Thinking 17
Applications of Statistical Thinking 22
Summary and Looking Forward 23
Exercises: Chapter 1 24
Notes 25
Chapter 2 Data: The Missing Link 27
Why Do We Need Data? 28
Types of Data 29
All Data are Not Created Equal 32
Practical Sampling Tips to Ensure Data Quality 34
What about Data Quantity? 38
Every Data Set Has a Story: The Data Pedigree 40
The Measurement System 42
Summarizing Data 48
Summary and Looking Forward 52
Exercises: Chapter 2 52
Notes 54
Chapter 3 Statistical Thinking Strategy 55
Case Study: The Effect of Advertising on Sales 56
Case Study: Improvement of a Soccer Team’s Performance 62
Statistical Thinking Strategy 71
Variation in Business Processes 76
Synergy between Data and Subject Matter Knowledge 82
Dynamic Nature of Business Processes 84
Value of Graphics—Discovering the Unexpected 86
Summary and Looking Forward 89
Project Update 89
Exercises: Chapter 3 90
Notes 91
Chapter 4 Understanding Business Processes 93
Examples of Business Processes 94
SIPOC Model for Processes 100
Identifying Business Processes 102
Analysis of Business Processes 103
Systems of Processes 119
Summary and Looking Forward 122
Project Update 123
Exercises: Chapter 4 124
Notes 126
Part Two Holistic Improvement: Frameworks and Basic Tools 127
Chapter 5 Holistic Improvement: Tactics to Deploy Statistical Thinking 129
Case Study: Resolving Customer Complaints of Baby Wipe Flushability 130
The Problem-Solving Framework 137
Case Study: Reducing Resin Output Variation 141
The Process Improvement Framework 147
Statistical Engineering 153
Statistical Engineering Case Study: Predicting Corporate Defaults 154
A Framework for Statistical Engineering Projects 158
Summary and Looking Forward 164
Project Update 165
Exercises: Chapter 5 166
Notes 167
Chapter 6 Process Improvement and Problem-Solving Tools 169
Practical Tools 172
Knowledge-Based Tools 191
Graphical Tools 207
Analytical Tools 228
Summary and Looking Forward 265
Project Update 265
Exercises: Chapter 6 266
Notes 271
Part Three Formal Statistical Methods 273
Chapter 7 Building and Using Models 275
Examples of Business Models 276
Types and Uses of Models 279
Regression Modeling Process 282
Building Models with One Predictor Variable 290
Building Models with Several Predictor Variables 307
Multicollinearity: Another Model Check 315
Some Limitations of Using Observational Data 317
Summary and Looking Forward 319
Project Update 321
Exercises: Chapter 7 321
Notes 346
Chapter 8 Using Process Experimentation to Build Models 347
Randomized versus Observational Studies 348
Why Do We Need a Statistical Approach? 350
Examples of Process Experiments 355
Problem-Solving and Process Improvement are Sequential 364
Statistical Approach to Experimentation 365
Two-Factor Experiments: A Case Study 372
Three-Factor Experiments: A Case Study 378
Larger Experiments 385
Blocking, Randomization, and Center Points 387
Summary and Looking Forward 389
Project Update 391
Exercises: Chapter 8 391
Notes 399
Chapter 9 Applications of Statistical Inference Tools 401
Examples of Statistical Inference Tools 404
Process of Applying Statistical Inference 408
Statistical Confidence and Prediction Intervals 412
Statistical Hypothesis Tests 424
Tests for Continuous Data 435
Test for Discrete Data: Comparing Two or More Proportions 441
Test for Regression Analysis: Test on a Regression Coefficient 442
Sample Size Formulas 443
Summary and Looking Forward 448
Project Update 449
Exercises: Chapter 9 450
Notes 454
Chapter 10 Underlying Theory of Statistical Inference 455
Applications of the Theory 456
Theoretical Framework of Statistical Inference 458
Probability Distributions 463
Sampling Distributions 479
Linear Combinations 486
Transformations 490
Summary and Looking Forward 510
Project Update 511
Exercises: Chapter 10 511
Notes 514
Appendix A Effective Teamwork 515
Appendix B Presentations and Report Writing 525
Appendix C More on Surveys 531
Appendix D More on Regression 539
Appendix E More on Design of Experiments 553
Appendix F More on Inference Tools 567
Appendix G More on Probability Distributions 571
Appendix H DMAIC Process Improvement Framework 577
Appendix I t Critical Values 587
Appendix J Standard Normal Probabilities (Cumulative z Curve Areas) 589
Index 593
About the Author :
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.
