Miller & Freund's Probability and Statistics for Engineers: International Edition (8e)

For an introductory, one or two semester, sophomore-junior level course in Probability and Statistics or Applied Statistics for engineering, physical science, and mathematics students.

 

This text is rich in exercises and examples, and explores both elementary probability and basic statistics, with an emphasis on engineering and science applications. Much of the data have been collected from the author's own consulting experience and from discussions with scientists and engineers about the use of statistics in their fields. In later chapters, the text emphasizes designed experiments, especially two-level factorial design.

Preface

 

1. Introduction

1.1 Why Study Statistics?

1.2 Modern Statistics

1.3 Statistics and Engineering

1.4 The Role of the Scientist and Engineer in Quality Improvement

1.5 A Case Study: Visually Inspecting Data to Improve Product Quality

1.6 Two Basic Concepts–Population and Sample

 

2. Organization and Description of Data

2.1 Pareto Diagrams and Dot Diagrams

2.2 Frequency Distributions

2.3 Graphs of Frequency Distributions

2.4 Stem-and-Leaf Displays

2.5 Descriptive Measures

2.6 Quartiles and Percentiles

2.7 The Calculation of x and s

2.8 A Case Study: Problems with Aggregating Data

 

3. Probability

3.1 Sample Spaces and Events

3.2 Counting

3.3 Probability

3.4 The Axioms of Probability

3.5 Some Elementary Theorems

3.6 Conditional Probability

3.7 Bayes’ Theorem

 

4. Probability Distributions

4.1 Random Variables

4.2 The Binomial Distribution

4.3 The Hypergeometric Distribution

4.4 The Mean and the Variance of a Probability Distribution

4.5 Chebyshev’s Theorem

4.6 The Poisson Approximation to the Binomial Distribution

4.7 Poisson Processes

4.8 The Geometric and Negative Binomial Distribution

4.9 The Multinomial Distribution

4.10 Simulation

 

5. Probability Densities

5.1 Continuous Random Variables

5.2 The Normal Distribution

5.3 The Normal Approximation to the Binomial Distribution

5.4 Other Probability Densities

5.5 The Uniform Distribution

5.6 The Log-Normal Distribution

5.7 The Gamma Distribution

5.8 The Beta Distribution

5.9 The Weibull Distribution

5.10 Joint Distributions–Discrete and Continuous

5.11 Moment Generating Functions

5.12 Checking If the Data Are Normal

5.13 Transforming Observations to Near Normality

5.14 Simulation

 

6. Sampling Distributions

6.1 Populations and Samples

6.2 The Sampling Distribution of the Mean (σ known)

6.3 The Sampling Distribution of the Mean (σ unknown)

6.4 The Sampling Distribution of the Variance

6.5 Representations of the Normal Theory Distributions

6.6 The Moment Generating Function Method to Obtain Distributions

6.7 Transformation Methods to Obtain Distributions

 

7. Inferences Concerning a Mean

7.1 Point Estimation

7.2 Interval Estimation

7.3 Maximum Likelihood Estimation

7.4 Tests of Hypotheses

7.5 Null Hypotheses and Tests of Hypotheses

7.6 Hypotheses Concerning One Mean

7.7 The Relation between Tests and Confidence Intervals

7.8 Power, Sample Size, and Operating Characteristic Curves

 

8. Comparing Two Treatments

8.1 Experimental Designs for Comparing Two Treatments

8.2 Comparisons–Two Independent Large Samples

8.3 Comparisons–Two Independent Small Samples

8.4 Matched Pairs Comparisons

8.5 Design Issues–Randomization and Pairing

 

9. Inferences Concerning Variances

9.1 The Estimation of Variances

9.2 Hypotheses Concerning One Variance

9.3 Hypotheses Concerning Two Variances

 

10. Inferences Concerning Proportions

10.1 Estimation of Proportions

10.2 Hypotheses Concerning One Proportion

10.3 Hypotheses Concerning Several Proportions

10.4 Analysis of r x c Tables

10.5 Goodness of Fit

 

11. Regression Analysis

11.1 The Method of Least Squares

11.2 Inferences Based on the Least Squares Estimators

11.3 Curvilinear Regression

11.4 Multiple Regression

11.5 Checking the Adequacy of the Model

11.6 Correlation

11.7 Multiple Linear Regression (Matrix Notation)

 

12. Analysis of Variance

12.1 Some General Principles

12.2 Completely Randomized Designs

12.3 Randomized-Block Designs

12.4 Multiple Comparisons

12.5 Analysis of Covariance

 

13. Factorial Experimentation

13.1 Two-Factor Experiments

13.2 Multifactor Experiments

13.3 2ⁿ Factorial Experiments

13.4 The Graphic Presentation of 22 and 23 Experiments

13.5 Response Surface Analysis

13.6 Confounding in a 2ⁿ Factorial Experiment

13.7 Fractional Replication

 

14. Nonparametric Tests

14.1 Introduction

14.2 The Sign Test

14.3 Rank-Sum Tests

14.4 Correlation Based on Ranks

14.5 Tests of Randomness

14.6 The Kolmogorov-Smirnov and Anderson-Darling Tests

 

15. The Statistical Content of Quality-Improvement Programs

15.1 Quality-Improvement Programs

15.2 Starting a Quality-Improvement Program

15.3 Experimental Designs for Quality

15.4 Quality Control

15.5 Control Charts for Measurements

15.6 Control Charts for Attributes

15.7 Tolerance Limits

 

16. Application to Reliability and Life Testing

16.1 Reliability

16.2 Failure-Time Distribution

16.3 The Exponential Model in Life Testing

16.4 The Weibull Model in Life Testing

 

Appendix A Bibliography

Appendix B Statistical Tables

Appendix C Using the R Software Program

Appendix D Answers to Odd-Numbered Exercises

 

• Increased number of exercises, data sets, and examples that use statistics in scientific investigations help students see how they can apply the concepts to their future careers. The new data sets, like many of those already in the text, arose in the author’s consulting activities or in discussions with scientists and engineers about their statistical problems.
• New Table 5.2 and Table 5.3 summarize discrete and continuous distributions, respectively.
• Chapter 7 (Inferences Concerning Means) has been split into a new Chapter 7 (Inferences Concerning a Mean) and Chapter 8 (Comparing Two Treatments) to improve both presentation and readability. Because of its popularity in engineering practice, maximum likelihood estimation is developed in a new Section 7.3.
• A new Section 13.5 (Response Surface Analysis) has been added to introduce the idea of response surface methodology through an example.
• Four new short sections have been added, which cover the essentials of a more mathematical treatment of distribution theory. These were motivated by the suggestions from many reviewers and users of the text. These new sections can be omitted without affecting the readability or comprehension, but an appreciation of the results can enhance the understanding of statistical procedures.
• Section 5.11 covers moment generating functions
• Section 6.6 covers the moment generating function method
• Section 6.5 covers representations and relations among the normal theory distributions
• Section 6.7 covers transformation methods for obtaining sampling distributions.

• Now online:
• All data sets from the book (www.pearsonhighered.com/datasets)
• Expanded details of some of the new sections:
• A new section on convolution methods
• Section 14.8 and its accompanying tables from Appendix B of the previous edition.
• Exercises are also included so instructors who to incorporate these topics into their courses can do so easily

 

• Clear, concise presentation helps students quickly gain an understanding of the concepts.
• Rich problem sets give students the practice they need to learn the material.
• Do's and Don'ts at the end of each chapterhelp students to apply statistics correctly to avoid misuses.
• Computer exercises for MINITAB^® help students learn and become familiar with this software.
• Many data sets are drawn from author Richard Johnson's own consulting activities as well as discussions with scientists and engineers about their statistical problems. This helps illustrate the statistical methods and reasoning required in order to draw generalizations from data collected in actual experiments.

• Content highlights:
• Case studies in the first two chapters illustrate the power of even simple statistical methods to suggest changes that make major improvements in production processes.
• Graphs of the sampling distribution show the critical region and P value, and accompany the examples of testing hypotheses. These graphs help reinforce student understanding of the critical region, significance level, and P value.
• Summary tables of testing procedures provide a convenient reference for students.
• A section on graphic presentation of 22 and 23 designs includes coverage of blocking. This serves as a stand-alone introduction to the design of experiments for those instructors who can only devote two or three lectures to the subject.
• Solid treatment of confidence interval techniques and hypothesis testing procedures, which clearly and consistently delineates the steps for hypothesis testing in each application.
• Clear, current coverage of two-level factorial design. To explore interactions, engineers have to know about experiments where more than one variable has been changed at the same time in design.
• A full chapter on modern ideas of quality improvement provides up-to-date coverage of this popular significant trend in the field.
• Accessible discussion on joint distributions and the properties of expectation--this is a difficult topic not always covered in the course, but if so desired, here is a nice, quick treatment of it.

 

Richard Johnson is the co-author of seven statistics texts and monographs, including Probability and Statistics for Engineers and Applied Multivariate Statistical Analysis. He was thefounding editor of Statistics and Probability Letters and served as editor for 25 years.Besides many years of experience in teaching all levels of statistics courses at the University of Wisconsin, he has published more than 120 technical papers concentrating in the areas of reliability and life testing, multivariate analysis, large sample theory, and applications to engineering. Johnson has presented talks on his research in 23 foreign counties. He is an elected member of the International Statistical Institute, a Fellow of the American Statistical Association, a Fellow of theInstitute of Mathematical Statistics, and a Fellow of the Royal Statistical Society.