Title Details

Douglas C. Montgomery, Regents' Professor of Industrial Engineering and Statistics at Arizona State University, received his B.S., M.S., and Ph.D. degrees from Virginia Polytechnic Institute, all in engineering. From 1969 to 1984, he was a faculty member of the School of Industrial & Systems Engineering at the Georgia Institute of Technology; from 1984 to 1988, he was at the University of Washington, where he held the John M. Fluke Distinguished chair of Manufacturing Engineering, was Professor of Mechanical Engineering, and Director of the Program in industrial Engineering. He has authored and coauthored many technical papers as well as twelve other books. Dr. Montgomery is a Stewart Medalist of the American Society for Quality, and has also received the Brumbaugh Award, the William G. Hunter Award, and the Shewell Award(twice) from the ASQ.

CHAPTER 1 The Role of Statistics in Engineering.

1-1 The Engineering Method and Statistical Thinking.

1-2 Collecting Engineering Data.

1-3 Mechanistic and Empirical Models.

1-4 Observing Processes Over Time.

CHAPTER 2 Data Summary and Presentation.

2-1 Data Summary and Display.

2-2 Stem-and-Leaf Diagram.

2-3 Histograms.

2-4 Box Plot.

2-5 Time Series Plots.

2-6 Multivariate Data.

CHAPTER 3 Random Variables and Probability Distributions.

3-1 Introduction.

3-2 Random Variables.

3-3 Probability.

3-4 Continuous Random Variables.

3-5 Important Continuous Distributions.

3-6 Probability Plots.

3-7 Discrete Random Variables.

3-8 Binomial Distribution.

3-9 Poisson Process.

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

3-11 More than One Random Variable and Independence.

3-12 Functions of Random Variables.

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

CHAPTER 4 Decision Making for a Single Sample.

4-1 Statistical Inference.

4-2 Point Estimation.

4-3 Hypothesis Testing.

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

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

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

4-7 Inference on a Population Proportion.

4-8 Other Interval Estimates for a Single Sample.

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

4-10 Testing for Goodness of Fit.

CHAPTER 5 Decision Making for Two Samples.

5-1 Introduction.

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

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

5-4 The Paired t-Test.

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

5-6 Inference on Two Population Proportions.

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

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

CHAPTER 6 Building Empirical Models.

6-1 Introduction to Empirical Models.

6-2 Simple Linear Regression.

6-3 Multiple Regression.

6-4 Other Aspects of Regression.

CHAPTER 7 Design of Engineering Experiments.

7-1 The Strategy of Experimentation.

7-2 Factorial Experiments.

7-3 2^k Factorial Design.

7-4 Center Points and Blocking in 2^k Designs.

7-5 Fractional Replication of a 2^k Design.

7-6 Response Surface Methods and Designs.

7-7 Factorial Experiments With More Than Two Levels.

CHAPTER 8 Statistical Process Control.

8-1 Quality Improvement and Statistical Process Control.

8-2 Introduction to Control Charts.

8-3 X and R Control Charts.

8-4 Control Charts For Individual Measurements.

8-5 Process Capability.

8-6 Attribute Control Charts.

8-7 Control Chart Performance.

8-8 Measurement Systems Capability.

APPENDICES.

APPENDIX A Statistical Tables and Charts.

APPENDIX B Bibliography.

APPENDIX C Answers to Selected Exercises.

INDEX.