Applied Statistics and Probability for Engineers, 6th Edition

Applied Statistics and Probability for Engineers, 6th Edition

by Douglas C. Montgomery, George C. Runger
Released November 2013
Publisher(s): Wiley
ISBN: 9781118539712

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Book description

This best-selling engineering statistics text provides a practical approach that is more oriented to engineering and the chemical and physical sciences than many similar texts. It is packed with unique problem sets that reflect realistic situations engineers will encounter in their working lives. This text shows how statistics, the science of data is just as important for engineers as the mechanical, electrical, and materials sciences.

Table of contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. Preface
    1. INTENDED AUDIENCE
    2. ORGANIZATION OF THE BOOK
    3. WHAT'S NEW IN THIS EDITION
    4. FEATURED IN THIS BOOK
    5. STUDENT RESOURCES
    6. INSTRUCTOR RESOURCES
    7. COMPUTER SOFTWARE
    8. WileyPLUS
    9. COURSE SYLLABUS SUGGESTIONS
    10. USING THE COMPUTER
    11. ACKNOWLEDGMENTS
  5. Contents
  6. 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
  7. 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
  8. 3: Discrete Random Variables and Probability Distributions
    1. 3-1 Discrete Random Variables
    2. 3-2 Probability Distributions and Probability Mass Functions
    3. 3-3 Cumulative Distribution Functions
    4. 3-4 Mean and Variance of a Discrete Random Variable
    5. 3-5 Discrete Uniform Distribution
    6. 3-6 Binomial Distribution
    7. 3-7 Geometric and Negative Binomial Distributions
    8. 3-8 Hypergeometric Distribution
    9. 3-9 Poisson Distribution
  9. 4: Continuous Random Variables and Probability Distributions
    1. 4-1 Continuous Random Variables
    2. 4-2 Probability Distributions and Probability Density Functions
    3. 4-3 Cumulative Distribution Functions
    4. 4-4 Mean and Variance of a Continuous Random Variable
    5. 4-5 Continuous Uniform Distribution
    6. 4-6 Normal Distribution
    7. 4-7 Normal Approximation to the Binomial and Poisson Distributions
    8. 4-8 Exponential Distribution
    9. 4-9 Erlang and Gamma Distributions
    10. 4-10 Weibull Distribution
    11. 4-11 Lognormal Distribution
    12. 4-12 Beta Distribution
  10. 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
    6. 5-6 Moment-Generating Functions
  11. 6: Descriptive Statistics
    1. 6-1 Numerical Summaries of Data
    2. 6-2 Stem-and-Leaf Diagrams
    3. 6-3 Frequency Distributions and Histograms
    4. 6-4 Box Plots
    5. 6-5 Time Sequence Plots
    6. 6-6 Scatter Diagrams
    7. 6-7 Probability Plots
  12. 7: Point Estimation of Parameters and Sampling Distributions
    1. Introduction
    2. 7-1 Point Estimation
    3. 7-2 Sampling Distributions and the Central Limit Theorem
    4. 7-3 General Concepts of Point Estimation
    5. 7-4 Methods of Point Estimation
  13. 8: Statistical Intervals for a Single Sample
    1. Introduction
    2. 8-1 Confidence Interval on the Mean of a Normal Distribution, Variance Known
    3. 8-2 Confidence Interval on the Mean of a Normal Distribution, Variance Unknown
    4. 8-3 Confidence Interval on the Variance and Standard Deviation of a Normal Distribution
    5. 8-4 Large-Sample Confidence Interval for a Population Proportion
    6. 8-5 Guidelines for Constructing Confidence Intervals
    7. 8.6 Bootstrap Confidence Interval
    8. 8-7 Tolerance and Prediction Intervals
  14. 9: Tests of Hypotheses for a Single Sample
    1. INTRODUCTION
    2. 9-1 Hypothesis Testing
    3. 9-2 Tests on the Mean of a Normal Distribution, Variance Known
    4. 9-3 Tests on the Mean of a Normal Distribution, Variance Unknown
    5. 9-4 Tests on the Variance and Standard Deviation of a Normal Distribution
    6. 9-5 Tests on a Population Proportion
    7. 9-6 Summary Table of Inference Procedures for a Single Sample
    8. 9-7 Testing for Goodness of Fit
    9. 9-8 Contingency Table Tests
    10. 9-9 Nonparametric Procedures
    11. 9-10 Equivalence Testing
    12. 9-11 Combining P -Values
  15. 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 Road Map for Inference Procedures for Two Samples
  16. 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
  17. 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
  18. 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
  19. 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 2 k Factorial Designs
    6. 14-6 Blocking and Confounding in the 2 k Design
    7. 14-7 FRACTIONAL REPLICATION OF THE 2 k DESIGN
    8. 14-8 Response Surface Methods and Designs
  20. 15: Statistical Quality Control
    1. Bowl of beads
    2. 15-1 Quality Improvement and Statistics
    3. 15-2 Introduction to Control Charts
    4. 15-3 and R or S Control Charts
    5. 15-4 Control Charts for Individual Measurements
    6. 15-5 Process Capability
    7. 15-6 Attribute Control Charts
    8. 15-7 Control Chart Performance
    9. 15-8 Time-Weighted Charts
    10. 15-9 Other SPC Problem-Solving Tools
    11. 15-10 Decision Theory
    12. 15-11 Implementing SPC
  21. Appendices
    1. Appendix A: Statistical Tables and Charts
    2. Appendix B: Bibliography
    3. Appendix C: Answers to Selected Exercises
  22. Glossary
  23. Index
  24. Index of Applications in Examples and Exercises

Product information

  • Title: Applied Statistics and Probability for Engineers, 6th Edition
  • Author(s): Douglas C. Montgomery, George C. Runger
  • Release date: November 2013
  • Publisher(s): Wiley
  • ISBN: 9781118539712