Statistical Thinking : Improving Business Performance

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  • Edition: 2nd
  • Format: Hardcover
  • Copyright: 2012-04-24
  • Publisher: Wiley

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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.

Author Biography

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.

Table of Contents

Prefacep. xiii
Introduction to JMPp. xvii
statistical Thinking Conceptsp. 1
Need for Business improvementp. 3
Today's Business Realities and the Need to Improvep. 4
We Now Have Two Jobs: A Model for Business Improvementp. 7
New Management Approaches Require Statistical Thinkingp. 10
Principles of Statistical Thinkingp. 15
Applications of Statistical Thinkingp. 18
Summaryp. 20
Notesp. 20
Statistical Thinking Strategyp. 23
Case Study: The Effect of Advertising on Salesp. 24
Case Study: Improvement of a Soccer Team's Performancep. 30
Statistical Thinking Strategyp. 39
Context of Statistical Thinking: Statistics Discipline as a Systemp. 43
Variation in Business Processesp. 45
Synergy between Data and Subject Matter Knowledgep. 50
Dynamic Nature of Business Processesp. 51
Summaryp. 53
Project Updatep. 53
Notesp. 54
Understanding Business Processesp. 55
Examples of Business Processesp. 56
SIPOC Model for Processesp. 62
Identifying Business Processesp. 64
Analysis of Business Processesp. 65
Systems of Processesp. 79
Measurement Processp. 82
Summaryp. 87
Project Updatep. 88
Notesp. 89
Statistical Engineering: Frameworks and Basic Toolsp. 91
Statistical Engineering: Tactics to Deploy Statistical Thinkingp. 93
Statistical Engineeringp. 94
Case Study: Reducing Resin Output Variationp. 95
Case Study: Reducing Telephone Waiting Time at a Bankp. 101
Basic Process Improvement Frameworkp. 105
Case Study: Resolving Customer Complaints of Baby Wipe Flushabilityp. 111
Case Study: The Realized Revenue Fiascop. 117
Basic Problem-Solving Frameworkp. 123
DMAIC Frameworkp. 128
DMAIC Case Study: Newspaper Accuracyp. 130
Summaryp. 137
Project Updatep. 137
Notesp. 138
Process improvement and Problem-Solving Toolsp. 139
Stratificationp. 141
Data Collection Toolsp. 142
Basic Graphical Analysis Toolsp. 156
Knowledge-Based Toolsp. 172
Process Stability and Capability Toolsp. 205
Summaryp. 226
Project Updatep. 227
Notesp. 227
Formal Statistical Methodsp. 229
Building and Using Modelsp. 231
Examples of Business Modelsp. 232
Types and Uses of Modelsp. 235
Regression Modeling Processp. 238
Building Models with One Predictor Variablep. 246
Building Models with Several Predictor Variablesp. 254
Multicollinearity: Another Model Checkp. 261
Some Limitations of Using Existing Datap. 264
Summaryp. 265
Project Updatep. 267
Notesp. 267
Using Process Experimentation to Build Modelsp. 269
Why Do We Need a Statistical Approach?p. 270
Examples of Process Experimentsp. 273
Statistical Approach to Experimentationp. 279
Two-Factor Experiments: A Case Studyp. 286
Three-Factor Experiments: A Case Studyp. 292
Larger Experimentsp. 299
Blocking, Randomization, and Center Pointsp. 301
Summaryp. 303
Project Updatep. 304
Notesp. 305
Applications of Statistical Inference Toolsp. 307
Examples of Statistical Inference Toolsp. 310
Process of Applying Statistical Inferencep. 314
Statistical Confidence and Prediction Intervalsp. 317
Statistical Hypothesis Testsp. 330
Tests for Continuous Datap. 339
Test for Discrete Data: Comparing Two or More Proportionsp. 344
Test for Regression Analysis: Test on a Regression Coefficientp. 345
Sample Size Formulasp. 346
Summaryp. 352
Project Updatep. 353
Notesp. 353
Underlying Theory of Statistical inferencep. 355
Applications of the Theoryp. 356
Theoretical Framework of Statistical Inferencep. 358
Types of Datap. 363
Probability Distributionsp. 366
Sampling Distributionsp. 382
Linear Combinationsp. 389
Transformationsp. 392
Summaryp. 411
Project Updatep. 411
Notesp. 412
Summary and Path Forwardp. 413
A Personal Case Study by Tom Pohlenp. 414
Review of the Statistical Thinking Approachp. 420
Text Summaryp. 422
Potential Next Steps to Deeper Understanding of Statistical Thinkingp. 425
Project Summary and Debriefingp. 427
Notesp. 427
Effective Teamworkp. 429
Presentations and Report Writingp. 439
More on Surveysp. 445
More on Regressionp. 453
More on Design of Experimentsp. 467
More on inference Toolsp. 479
More on Probability Distributionsp. 483
Process Design (Reengineering)p. 491
t Critical Valuesp. 497
Standard Normal Probabilities (Cumulative z Curve Areas)p. 499
Indexp. 503
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