Probability & Statistics for Engineers & Scientists

AUTHOR :

Raymond H Myers is currently Professor Emeritus of statistics at Virginia Tech. He received his Masters and Ph.D. from Virginia Tech in statistics and his BS in chemical engineering. His major areas of interest are linear models, design of experiments, and response surface methodology. He has authored or co-authored six statistics texts that were published in fifteen separate editions and in several foreign languages.
He has received numerous teaching awards and in 1985 he was selected “Professor of the Year” in the state of Virginia by the Council on the Advancement and Support of Education. He was elected Fellow of ASA in 1974. In 1999 he was given the Shewhart Award for lifetime contributions in statistics and quality control by the American Society of Quality.

Sharon L Myers

is currently Professor Emeritus of mathematics and statistics at Radford University. She received her MS in statistics from Virginia Tech. Her areas of interest are statistical computing, regression analysis, and response surface methodology. She has co-authored three editions of “Probability & Statistics for Engineers & Scientists”. She was the assistant director of the statistical consulting center at Virginia Tech for 15 years and the director of the statistical consulting center at Radford University for 7 years.

Keying Ye, University of Texas at San Antonio

DESCRIPTION : 

For junior/senior undergraduates taking probability and statistics as it applied to engineering, science or computer science.
With its unique balance of theory and methodology, this classic text provides a rigorous introduction to basic probability theory and statistical inference that is motivated by interesting, relevant applications. Extensively updated coverage, new problem sets, and chapter-ending material extend the text’s relevance to a new generation of engineers and scientists.

Probability & Statistics for Engineers & Scientists

 

 

CONTENTS :
1. Introduction to Statistics and Data Analysis
2. Probability
3. Random Variables and Probability Distributions
4. Mathematical Expectations
5. Some Discrete Probability Distributions
6. Some Continuous Probability Distributions
7. Functions of Random Variables (optional)
8. Fundamental Distributions and Data Description
9. One and Two Sample Estimation Problems
10. One and Two Sided Tests of Hypotheses
11. Simple Linear Regression
12. Multiple Linear Regression
13. One Factor Experiments: General
14. Factorial Experiments (Two or More Factors)
15. 2k Factorial Experiments and Fractions
16. Nonparametric Statistics
17. Statistical Quality Control
18. Bayesian Statistics

FEATURES :

¿ Designed for a one or two semester course. A reasonable one semester course might include chapters 1-10. Flexibility exists, in terms of coverage of topics, which facilitates any one semester course based on the priorities set down by the instructor.
¿ Offers a balance between theory and applications — Engineers, physical scientists and computer scientists are trained in calculus and thus mathematical support is given when the authors feel as if the pedagogy is enhanced by it. This approach prohibits the material from becoming a collection of tools with no mathematical roots.
¿ Mathematical Level — The use of calculus is confined to elementary probability theory and probability distributions (chapters 2-7). Matrix algebra is used only a modest amount in linear regression material (chapters 11-12). Students using this text should have completed the equivalent of one semester of differential and integral calculus. An exposure to matrix algebra would be helpful but not necessary if the course context excludes the optional section in chapter 12.
¿ Accurate, compelling exercise sets — Use significant real data from actual studies (biomedical, bioengineering, business, computing, etc). . The exercises challenge the student to be able to use the concepts from the text to solve problems dealing with many real-life scientific and engineering situations.

¿ Case Studies and Computer Software — The topical material in two-sample hypothesis testing, multiple linear regression, analysis of variance, and the use of two-level factorial-experiments is supplemented by case studies that feature computer printout and graphical material. Both SAS and MINITAB are featured.
Chapter Outline:
Chapter 1: Elementary overview of statistical inference.
Chapters 2 — 4: Deal with basic probability as well as discrete and continuous random variables.
Chapters 5-6: Cover specific discrete and continuous distributions with illustrations of their use and relationships among them.
Chapter 7: Optional chapter that treats transformation of random variables.
Chapter 8: Additional materials on graphical methods as well as a very important introduction to the notion of sampling distribution
Chapters 9 — 10: Contain material on one and two sample point and interval estimation and