Statistical Thinking: Improving Business Performance(Wiley and SAS Business Series)

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.