What is included with this book?
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 |
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