Engineering Statistics / Edition 5

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Overview

Montgomery, Runger, and Hubele provide modern coverage of engineering statistics, focusing on how statistical tools are integrated into the engineering problem-solving process.  All major aspects of engineering statistics are covered, including descriptive statistics, probability and probability distributions, statistical test and confidence intervals for one and two samples, building regression models, designing and analyzing engineering experiments, and statistical process control.  Developed with sponsorship from the National Science Foundation, this revision incorporates many insights from the authors’ teaching experience along with feedback from numerous adopters of previous editions.

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Editorial Reviews

Booknews
The authors have made a selection and revision from Montgomery and Runger's more substantial to produce the present text, which is designed to serve a one-semester course for undergraduate engineering students who have had at least one semester of calculus. Meant for an applied course, the text is designed to be used in conjunction with model- building and the computer (by using Minitab, Statgraphics and Excel). All three authors teach at Arizona State University. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780470631478
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 12/20/2010
  • Edition description: New Edition
  • Edition number: 5
  • Pages: 544
  • Sales rank: 204,889
  • Product dimensions: 8.30 (w) x 10.10 (h) x 0.90 (d)

Meet 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, 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 in the department 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 of Engineering and Statistics at Arizona State University, and Director of Strategic Initiatives for the Ira A. Fulton School of Engineering, has taught and done research at the University level and with industry for over 20 years. Her cutting edge educational research has been supported by the National Science Foundation and is reflected in this textbook.

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Table of Contents

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 2k Factorial Design.

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

7-5 Fractional Replication of a 2k 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.

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