Statistical Thinking: Improving Business Performance, 2nd Edition
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Statistical Thinking Improving Business Performance, 2nd Edition

Edited by Roger W. Hoerl · Ronald D. Snee
John Wiley & Sons April 2012

Specifications

ISBN-13
9781118094778
Publisher
John Wiley & Sons
Publication
April 2012
Format
Hardback , 511 pages
Jurisdiction
International ? Countri(es) for reference only

Details

How statistical thinking and methodology can help you make crucial business decisions

Straightforward and insightful, Statistical Thinking: Improving Business Performance, Second Edition, prepares you for business leadership by developing your capacity to apply statistical thinking to improve business processes. Unique and compelling, this book shows you how to derive actionable conclusions from data analysis, solve real problems, and improve real processes. Here, you'll discover how to implement statistical thinking and methodology in your work to improve business performance.

  • Explores why statistical thinking is necessary and helpful
  • Provides case studies that illustrate how to integrate several statistical tools into the decision-making process
  • Facilitates and encourages an experiential learning environment to enable you to apply material to actual problems

With an in-depth discussion of JMP® software, the new edition of this important book focuses on skills to improve business processes, including collecting data appropriate for a specified purpose, recognizing limitations in existing data, and understanding the limitations of statistical analyses.

Table of Contents

Preface

Part One Statistical Thinking Concepts

Chapter 1 Need for Business Improvement

Today's Business Realities and the Need to Improve

We Now Have Two Jobs: A Model for Business Improvement

New Management Approaches Require Statistical Thinking

Principles of Statistical Thinking

Applications of Statistical Thinking

Summary

Chapter 2 Statistical Thinking Strategy

CASE STUDY: The Effect of Advertising on Sales

CASE STUDY: Improvement of a Soccer Team's Performance

Statistical Thinking Strategy

Context of Statistical Thinking: Statistics Discipline as a System

Variation in Business Processes

Synergy Between Data and Subject Matter Knowledge

Dynamic Nature of Business Processes

Summary

Project Update

Chapter 3 Understanding Business Processes

Examples of Business Processes

SIPOC Model for Processes

Identifying Business Processes

Analysis of Business Processes

Systems of Processes

Measurement Process

Summary

Project Update

Part Two Statistical Engineering: Frameworks and Basic Tools

Chapter 4 Statistical Engineering: Tactics to Deploy Statistical Thinking

Statistical Engineering

CASE STUDY: Reducing Resin Output Variation

CASE STUDY: Reducing Telephone Waiting Time at a Bank

Basic Process Improvement Framework

CASE STUDY: Resolving Customer Complaints of Baby Wipe Flushability

CASE STUDY: The Realized Revenue Fiasco

Basic Problem-Solving Framework

DMAIC Framework

DMAIC Case Study: Newspaper Accuracy

Summary

Project Update

Chapter 5 Process Improvement and Problem-Solving Tools

Stratification

Data Collection Tools

Basic Graphical Analysis Tools

Knowledge-Based Tools

Process Stability and Capability Tools

Summary

Project Update

Part Three Formal Statistical Methods

Chapter 6 Building and Using Models

Examples of Business Models

Types and Uses of Models

Regression Modeling Process

Building Models with One Predictor Variable

Building Models with Several Predictor Variables

Multicollinearity, another Model Check

Some Limitations of Using Existing Data

Summary

Project Update

Chapter 7 Using Process Experimentation to Build Models

Why Do We Need a Statistical Approach?

Examples of Process Experiments

Statistical Approach to Experimentation

Two-Factor Experiments: A Case Study

Three-Factor Experiments: A Case Study

Larger Experiments

Blocking, Randomization, and Center Points

Summary

Project Update

Chapter 8 Applications of Statistical Inference Tools

Examples of Statistical Inference Tools

Process of Applying Statistical Inference

Statistical Confidence and Prediction Intervals

Statistical Hypothesis Tests

Tests for Continuous Data

Test for Discrete Data: Comparing Two or More Proportions

Test for Regression Analysis: Test on a Regression Coefficient

Sample Size Formulas

Summary

Project Update

Chapter 9 Underlying Theory of Statistical Inference

Applications of the Theory

Theoretical Framework of Statistical Inference

Types of Data

Probability Distributions

Sampling Distributions

Linear Combinations

Transformations

Summary

Project Update

Chapter 10 Summary and Path Forward

A Personal Case Study by Tom Pohlen

Review of the Statistical Thinking Approach

Text Summary

Potential Next Steps to Deeper Understanding of Statistical Thinking

Project Summary and Debriefing

Appendices

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 Distribution

Appendix H Process Design (Reengineering)

Appendix I t Critical Values

Appendix J Standard Normal Probabilities (Cumulative z Curve Areas)

Index

About the Author

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.

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