You are previewing Applied Statistics and Probability for Engineers, 5th Edition.
by George C. Runger... Published by John Wiley & Sons
1. Coverpage
2. Titlepage
4. Contents
5. Preface
6. INSIDE FRONT COVER Index of Applications in Examples and Exercises
7. CHAPTER 1 The Role of Statistics in Engineering
1. 1-1 The Engineering Method and Statistical Thinking
2. 1-2 Collecting Engineering Data
3. 1-3 Mechanistic and Empirical Models
4. 1-4 Probability and Probability Models
8. CHAPTER 2 Probability
1. 2-1 Sample Spaces and Events
2. 2-2 Interpretations and Axioms of Probability
3. 2-3 Addition Rules
4. 2-4 Conditional Probability
5. 2-5 Multiplication and Total Probability Rules
6. 2-6 Independence
7. 2-7 Bayes’ Theorem
8. 2-8 Random Variables
9. CHAPTER 3 Discrete Random Variables and Probability Distributions
10. CHAPTER 4 Continuous Random Variables and Probability Distributions
11. CHAPTER 5 Joint Probability Distributions
1. 5-1 Two or More Random Variables
2. 5-2 Covariance and Correlation
3. 5-3 Common Joint Distributions
4. 5-4 Linear Functions of Random Variables
5. 5-5 General Functions of Random Variables
12. CHAPTER 6 Descriptive Statistics
13. CHAPTER 7 Sampling Distributions and Point Estimation of Parameters
1. 7-1 Point Estimation
2. 7-2 Sampling Distributions and the Central Limit Theorem
3. 7-3 General Concepts of Point Estimation
4. 7-4 Methods of Point Estimation
14. CHAPTER 8 Statistical Intervals for a Single Sample
1. 8-1 Confidence Interval on the Mean of a Normal Distribution, Variance Known
2. 8-2 Confidence Interval on the Mean of a Normal Distribution, Variance Unknown
3. 8-3 Confidence Interval on the Variance and Standard Deviation of a Normal Distribution
4. 8-4 Large-Sample Confidence Interval for a Population Proportion
5. 8-5 Guidelines for Constructing Confidence Intervals
6. 8-6 Tolerance and Prediction Intervals
15. CHAPTER 9 Tests of Hypotheses for a Single Sample
1. 9-1 Hypothesis Testing
2. 9-2 Tests on the Mean of a Normal Distribution, Variance Known
3. 9-3 Tests on the Mean of a Normal Distribution, Variance Unknown
4. 9-4 Tests on the Variance and Standard Deviation of a Normal Distribution
5. 9-5 Tests on a Population Proportion
6. 9-6 Summary Table of Inference Procedures for a Single Sample
7. 9-7 Testing for Goodness of Fit
8. 9-8 Contingency Table Tests
9. 9-9 Nonparametric Procedures
16. CHAPTER 10 Statistical Inference for Two Samples
1. 10-1 Inference on the Difference in Means of Two Normal Distributions, Variances Known
2. 10-2 Inference on the Difference in Means of Two Normal Distributions, Variances Unknown
3. 10-3 A Nonparametric Test for the Difference in Two Means
4. 10-4 Paired t-Test
5. 10-5 Inference on the Variances of Two Normal Distributions
6. 10-6 Inference on Two Population Proportions
7. 10-7 Summary Table and Roadmap for Inference Procedures for Two Samples
17. CHAPTER 11 Simple Linear Regression and Correlation
1. 11-1 Empirical Models
2. 11-2 Simple Linear Regression
3. 11-3 Properties of the Least Squares Estimators
4. 11-4 Hypothesis Tests in Simple Linear Regression
5. 11-5 Confidence Intervals
6. 11-6 Prediction of New Observations
7. 11-7 Adequacy of the Regression Model
8. 11-8 Correlation
9. 11-9 Regression on Transformed Variables
10. 11-10 Logistic Regression
18. CHAPTER 12 Multiple Linear Regression
1. 12-1 Multiple Linear Regression Model
2. 12-2 Hypothesis Tests in Multiple Linear Regression
3. 12-3 Confidence Intervals in Multiple Linear Regression
4. 12-4 Prediction of New Observations
5. 12-5 Model Adequacy Checking
6. 12-6 Aspects of Multiple Regression Modeling
19. CHAPTER 13 Design and Analysis of Single-Factor Experiments: The Analysis of Variance
1. 13-1 Designing Engineering Experiments
2. 13-2 Completely Randomized Single-Factor Experiment
3. 13-3 The Random-Effects Model
4. 13-4 Randomized Complete Block Design
20. CHAPTER 14 Design of Experiments with Several Factors
1. 14-1 Introduction
2. 14-2 Factorial Experiments
3. 14-3 Two-Factor Factorial Experiments
4. 14-4 General Factorial Experiments
5. 14-5 2k Factorial Designs
6. 14-6 Blocking and Confounding in the 2k Design
7. 14-7 Fractional Replication of the 2k Design
8. 14-8 Response Surface Methods and Designs
21. CHAPTER 15 Statistical Quality Control
1. 15-1 Quality Improvement and Statistics
2. 15-2 Introduction to Control Charts
3. 15-3 X and R or S Control Charts
4. 15-4 Control Charts for Individual Measurements
5. 15-5 Process Capability
6. 15-6 Attribute Control Charts
7. 15-7 Control Chart Performance
8. 15-8 Time-Weighted Charts
9. 15-9 Other SPC Problem-Solving Tools
10. 15-10 Implementing SPC
22. APPENDICES
1. APPENDIX A: Statistical Tables and Charts
2. APPENDIX B: Answers to Selected Exercises
3. APPENDIX C: Bibliography
23. GLOSSARY
24. INDEX
25. INDEX OF APPLICATIONS IN EXAMPLES AND EXERCISES, CONTINUED

Bibliography

INTRODUCTORY WORKS AND GRAPHICAL METHODS

Chambers, J., Cleveland, W., Kleiner, B., and P. Tukey (1983), Graphical Methods for Data Analysis, Wadsworth & Brooks/Cole, Paciﬁc Grove, CA. A very well-written presentation of graphical methods in statistics.

Freedman, D., Pisani, R., Purves R., and A. Adbikari (1991), Statistics, 2nd ed., Norton, New York. An excellent introduction to statistical thinking, requiring minimal mathematical background.

Hoaglin, D., Mosteller, F., and J. Tukey (1983), Understanding Robust and Exploratory Data Analysis, John Wiley & Sons, New York. Good discussion and illustration of techniques such as stem-and-leaf displays and box plots.

Tanur, J., et al. (eds.) (1989), Statistics: A Guide to the Unknown, 3rd edition, Wadsworth & Brooks/Cole, Pacific Grove, CA. Contains a collection of short nonmathematical articles describing different applications of statistics.

Tukey, J. (1977), Exploratory Data Analysis, Addison-Wesley, Reading, MA. Introduces many new descriptive and analytical methods. Not extremely easy to read.

PROBABILITY

Hoel, P. G., Port, S. C., and C. J. Stone (1971), Introduction to Probability Theory, Houghton Mifflin, Boston. A well-written and comprehensive treatment of probability theory and the standard discrete and continuous distributions.

Olkin, I., Derman, C., and L. Gleser (1994), Probability Models and Applications, 2nd ed., Macmillan, New York. A comprehensive treatment of probability at a higher mathematical ...