DescriptionMontgomery, Runger, and Hubele's Engineering Statistics, 5th Edition provides modern coverage of engineering statistics by 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. This edition features new introductions, revised content to help students better understand ANOVA, new examples to help calculate probability and approximately 80 new exercises.
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.
2-4 Box Plot.
2-5 Time Series Plots.
2-6 Multivariate Data.
CHAPTER 3 Random Variables and Probability Distributions.
3-2 Random Variables.
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.
5-2 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.
APPENDIX A Statistical Tables and Charts.
APPENDIX B Bibliography.
APPENDIX C Answers to Selected Exercises.
- New introductions in each chapter demonstrate the relevance of statistics to engineering
- Design of experiments content revised and inclusion of additional material helps students to better interpret computer software output related to ANOVA
- New examples demonstrate calculating probability in Excel (Chapter 3)
- Practical interpretations in example problems provide improved linking of statistical conclusions to the actual engineering decision outcome
- Approximately 80 new exercises related to biology and health care (in most chapters)
- Accessible and manageable - presents important topics that can be covered in one semester
- Emphasis on professional practice - students get the coverage most useful to engineers with strong emphasis on data analysis and statistical inference with less focus on distributions and probability
- A strong focus on P-value approach to hypothesis testing familiarizes students with practical approaches to engineering problems
- Provides real engineering situations and applications as well as real data sets in examples and homework that help motivate students by applying statistics to the engineering profession
- A step-by-step problem solving approach integrated into examples
- Supports an active learning environment - reading questions and homework problems in WileyPlus help motivate and build student confidence with instant feedback. The supplemental problems and team exercises in the text can be used for in-class team activities, including designing experiments, generating data, and performing analyses.
- End of section exercises are structured to reinforce the concepts and technigues introduced in each section
- End of chapter supplemental exercises integrate concepts and techniques from multiple sections and reinforce student mastery of these concepts
- Team exercises challenge students to apply chapter methods and concepts to problems requiring data collection
- Software output in MINITAB throughout the text demonstrates the capability of modern statistical software