Table of Contents

CHAPTER 1 The Role of Statistics in Engineering 1

1-1 The Engineering Method and Statistical Thinking 2

1-2 Collecting Engineering Data 6

1-3 Mechanistic and Empirical Models 15

1-4 Observing Processes Over Time 17

CHAPTER 2 Data Summary and Presentation 23

2-1 Data Summary and Display 24

2-2 Stem-and-Leaf Diagram 29

2-3 Histograms 34

2-4 Box Plot 39

2-5 Time Series Plots 41

2-6 Multivariate Data 46

CHAPTER 3 Random Variables and Probability Distributions 57

3-1 Introduction 58

3-2 Random Variables 60

3-3 Probability 62

3-4 Continuous Random Variables 66

3-5 Important Continuous Distributions 74

3-6 Probability Plots 92

3-7 Discrete Random Variables 97

3-8 Binomial Distribution 102

3-9 Poisson Process 109

3-10 Normal Approximation to the Binomial and Poisson Distributions 119

3-11 More than One Random Variable and Independence 123

3-12 Functions of Random Variables 129

3-13 Random Samples, Statistics, and the Central Limit Theorem 136

CHAPTER 4 Decision Making for a Single Sample 148

4-1 Statistical Inference 149

4-2 Point Estimation 150

4-3 Hypothesis Testing 156

4-4 Inference on the Mean of a Population, Variance Known 169

4-5 Inference on the Mean of a Population, Variance Unknown 186

4-6 Inference on the Variance of a Normal Population 199

4-7 Inference on a Population Proportion 205

4-8 Other Interval Estimates for a Single Sample 216

4-9 Summary Tables of Inference Procedures for a Single Sample 219

4-10 Testing for Goodness of Fit 219

CHAPTER 5 Decision Making for Two Samples 230

5-1 Introduction 231

5-2 Inference on the Means of Two Populations, Variances Known 232

5-3 Inference on the Means of Two Populations, Variances Unknown 239

5-4 The Paired t-Test 252

5-5 Inference on the Ratio of Variances of Two Normal Populations 259

5-6 Inference on Two Population Proportions 265

5-7 Summary Tables for Inference Procedures for Two Samples 271

5-8 What if We Have More than Two Samples? 272

CHAPTER 6 Building Empirical Models 298

6-1 Introduction to Empirical Models 299

6-2 Simple Linear Regression 304

6-3 Multiple Regression 326

6-4 Other Aspects of Regression 344

CHAPTER 7 Design of Engineering Experiments 360

7-1 The Strategy of Experimentation 361

7-2 Factorial Experiments 362

7-3 2k Factorial Design 365

7-4 Center Points and Blocking in 2k Designs 390

7-5 Fractional Replication of a 2k Design 398

7-6 Response Surface Methods and Designs 414

7-7 Factorial Experiments With More Than Two Levels 424

CHAPTER 8 Statistical Process Control 438

8-1 Quality Improvement and Statistical Process Control 439

8-2 Introduction to Control Charts 440

8-3 and R Control Charts 449

8-4 Control Charts For Individual Measurements 456

8-5 Process Capability 461

8-6 Attribute Control Charts 465

8-7 Control Chart Performance 470

8-8 Measurement Systems Capability 473

APPENDICES 483

APPENDIX A Statistical Tables and Charts 485

APPENDIX B Bibliography 500

APPENDIX C Answers to Selected Exercises 502

INDEX 511

About the Author

Douglas C. Montgomery, Regents' Professor of Industrial Engineering and Statistics at Arizona State University, received his B.S., M.S., and Ph.D. degrees in engineering from Virginia Polytechnic Institute. He has been a faculty member of the School of Industrial and Systems Engineering at the Georgia Institute of Technology and a professor of mechanical engineering and director of the Program in Industrial Engineering at the University of Washington, where he held the John M. Fluke Distinguished Chair of Manufacturing Engineering. The recipient of numerous awards including the Deming Lecture Award from the American Statistical Association, Shewhart Medal of the American Society for Quality, the George Box medal from EENBIS, the Greenfield medal from the Royal Statistical Society, the Brumbaugh Award, the Lloyd S. Nelson Award, the William G. Hunter Award, and two Shewell Awards from the ASQ. He is the editor of Quality and Reliability Engineering International and a former editor of the Journal of Quality Technology.

George C. Runger, Ph.D., is a Professor of Industrial Engineering at Arizona State University. His research is on data mining, real-time monitoring and control, and other data-analysis methods with a focus on large, complex, multivariate data streams. His work is funded by grants from the National Science Foundation and corporations. In addition to academic work, he was a senior engineer at IBM. He holds degrees in industrial engineering and statistics.

Norma Faris Hubele, Professor Emeritus of Engineering and Statistics at Arizona State University, and formerly Director of Strategic Initiatives for the Ira A. Fulton School of Engineering, holds degrees in mathematics, operations research, statistics and computer and systems engineering. She is co-owner of the metallurgical processing and statistical consulting company Refrac Systems in Chandler, Arizona. She is on the editorial board of the Journal of Quality Technology and Quality Technology & Quantity Management, as a founding member. Her specializations include capability analysis, transportation safety, and statistics in litigation.