Statistical Thinking Improving Business

This book shows both managers who are learning statistic to improve initiatives in business and industry as well as students (graduate and undergraduate) how to understand the strategic value of data and statistics in solving real business problems. Following principles of effective learning identified by educational and behavioral research, the instruction proceeds from tangible examples to abstract theory; from the big picture, or "whole," to details, or "parts"; and from a conceptual understanding to ability to perform specific tasks. The authors teach skills to improve business processes, including collecting data appropriate for a specified purpose, recognizing limitations in existing data, graphically analyzing data using basic tools, deriving actionable conclusions from data analyses, and understanding the limitations of statistical analyses. In addition, the authors will be providing an in-depth discussion of JMP software (what it is, what it can do, and how to use it). Whenever graphics or statistical tools are introduced, the book will provide the examples in JMP output.

ROGER HOERL leads the Applied Statistics Laboratory at GE Global Research, which focuses on new product and service development within each of the GE businesses. In 2006, he received the Coolidge Fellowship from GE Global Research, honoring one scientist a year from among the four global GE Research and Development sites for lifetime technical achievement. Dr. Hoerl has authored five books in the areas of statistics and business improvement, two book chapters, and over thirty-five refereed journal articles.

RON SNEE is founder and President of Snee Associates, an authority on designing and implementing improvement and cost reduction solutions for a variety of organizational environments. Dr. Snee has an outstanding record of leadership in process and organizational improvement in a variety of industries including pharmaceutical, biotech, clinical diagnostics, and telecommunications. Among his other achievements, he is credited with leading the design of the first company-wide continuous improvement curriculum for the global giant E. I. DuPont de Nemours. He holds a host of awards and honors, has coauthored four books, and published more than 200 articles on process improvement, quality, management, and statistics.

Preface	p. xiii
Introduction to JMP	p. xvii
statistical Thinking Concepts	p. 1
Need for Business improvement	p. 3
Today's Business Realities and the Need to Improve	p. 4
We Now Have Two Jobs: A Model for Business Improvement	p. 7
New Management Approaches Require Statistical Thinking	p. 10
Principles of Statistical Thinking	p. 15
Applications of Statistical Thinking	p. 18
Summary	p. 20
Notes	p. 20
Statistical Thinking Strategy	p. 23
Case Study: The Effect of Advertising on Sales	p. 24
Case Study: Improvement of a Soccer Team's Performance	p. 30
Statistical Thinking Strategy	p. 39
Context of Statistical Thinking: Statistics Discipline as a System	p. 43
Variation in Business Processes	p. 45
Synergy between Data and Subject Matter Knowledge	p. 50
Dynamic Nature of Business Processes	p. 51
Summary	p. 53
Project Update	p. 53
Notes	p. 54
Understanding Business Processes	p. 55
Examples of Business Processes	p. 56
SIPOC Model for Processes	p. 62
Identifying Business Processes	p. 64
Analysis of Business Processes	p. 65
Systems of Processes	p. 79
Measurement Process	p. 82
Summary	p. 87
Project Update	p. 88
Notes	p. 89
Statistical Engineering: Frameworks and Basic Tools	p. 91
Statistical Engineering: Tactics to Deploy Statistical Thinking	p. 93
Statistical Engineering	p. 94
Case Study: Reducing Resin Output Variation	p. 95
Case Study: Reducing Telephone Waiting Time at a Bank	p. 101
Basic Process Improvement Framework	p. 105
Case Study: Resolving Customer Complaints of Baby Wipe Flushability	p. 111
Case Study: The Realized Revenue Fiasco	p. 117
Basic Problem-Solving Framework	p. 123
DMAIC Framework	p. 128
DMAIC Case Study: Newspaper Accuracy	p. 130
Summary	p. 137
Project Update	p. 137
Notes	p. 138
Process improvement and Problem-Solving Tools	p. 139
Stratification	p. 141
Data Collection Tools	p. 142
Basic Graphical Analysis Tools	p. 156
Knowledge-Based Tools	p. 172
Process Stability and Capability Tools	p. 205
Summary	p. 226
Project Update	p. 227
Notes	p. 227
Formal Statistical Methods	p. 229
Building and Using Models	p. 231
Examples of Business Models	p. 232
Types and Uses of Models	p. 235
Regression Modeling Process	p. 238
Building Models with One Predictor Variable	p. 246
Building Models with Several Predictor Variables	p. 254
Multicollinearity: Another Model Check	p. 261
Some Limitations of Using Existing Data	p. 264
Summary	p. 265
Project Update	p. 267
Notes	p. 267
Using Process Experimentation to Build Models	p. 269
Why Do We Need a Statistical Approach?	p. 270
Examples of Process Experiments	p. 273
Statistical Approach to Experimentation	p. 279
Two-Factor Experiments: A Case Study	p. 286
Three-Factor Experiments: A Case Study	p. 292
Larger Experiments	p. 299
Blocking, Randomization, and Center Points	p. 301
Summary	p. 303
Project Update	p. 304
Notes	p. 305
Applications of Statistical Inference Tools	p. 307
Examples of Statistical Inference Tools	p. 310
Process of Applying Statistical Inference	p. 314
Statistical Confidence and Prediction Intervals	p. 317
Statistical Hypothesis Tests	p. 330
Tests for Continuous Data	p. 339
Test for Discrete Data: Comparing Two or More Proportions	p. 344
Test for Regression Analysis: Test on a Regression Coefficient	p. 345
Sample Size Formulas	p. 346
Summary	p. 352
Project Update	p. 353
Notes	p. 353
Underlying Theory of Statistical inference	p. 355
Applications of the Theory	p. 356
Theoretical Framework of Statistical Inference	p. 358
Types of Data	p. 363
Probability Distributions	p. 366
Sampling Distributions	p. 382
Linear Combinations	p. 389
Transformations	p. 392
Summary	p. 411
Project Update	p. 411
Notes	p. 412
Summary and Path Forward	p. 413
A Personal Case Study by Tom Pohlen	p. 414
Review of the Statistical Thinking Approach	p. 420
Text Summary	p. 422
Potential Next Steps to Deeper Understanding of Statistical Thinking	p. 425
Project Summary and Debriefing	p. 427
Notes	p. 427
Effective Teamwork	p. 429
Presentations and Report Writing	p. 439
More on Surveys	p. 445
More on Regression	p. 453
More on Design of Experiments	p. 467
More on inference Tools	p. 479
More on Probability Distributions	p. 483
Process Design (Reengineering)	p. 491
t Critical Values	p. 497
Standard Normal Probabilities (Cumulative z Curve Areas)	p. 499
Index	p. 503
Table of Contents provided by Ingram. All Rights Reserved.

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Statistical Thinking Improving Business Performance

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