Probability & Statistics for Engineers & Scientists: United States Edition
Autor Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, Keying Yeen Limba Engleză Hardback – 22 feb 2006
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.
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Specificații
ISBN-13: 9780131877115
ISBN-10: 0131877119
Pagini: 848
Dimensiuni: 191 x 235 mm
Greutate: 1.41 kg
Ediția:8Nouă
Editura: Pearson Education
Colecția Pearson Education
Locul publicării:Upper Saddle River, United States
ISBN-10: 0131877119
Pagini: 848
Dimensiuni: 191 x 235 mm
Greutate: 1.41 kg
Ediția:8Nouă
Editura: Pearson Education
Colecția Pearson Education
Locul publicării:Upper Saddle River, United States
Cuprins
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
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
Notă biografică
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
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
Caracteristici
¿ 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
Caracteristici noi
• Updated problem sets and applications – Includes nearly 20% new problem sets that demonstrate updated applications to engineering as well as biological, physical, and computer science.
• New end-of-chapter review material – Emphasizes key ideas as well as the risks and hazards associated with practical application of the material.
• New self-contained chapter on Bayesian statistics – Takes a practical approach by introducing applications in many fields. Introduces the concept of subjective probability, and treats point and interval estimation from a Bayesian point of view using practical examples.
• Extensive updates throughout – Includes new material on topics such as: difference between discrete and continuous measurements; binary data; quartiles; importance of experimental design; “dummy” variables; rules for expectations and variances of linear functions; Poisson distribution; Weibull and lognormal distributions; central limit theorem, and data plotting.
• New exercises on random variables and probability distributions – Use compelling topics such as particle size distribution for missile fuel; measurement errors in scientific systems; studies of time to failure for washing machines; assembly-line production of electron tubes; product shelf life; airport passenger congestion; chemical impurities; failure in systems of electronic components working in parallel, and many others.
• Restructured coverage of hypothesis testing – Helps students develop a clear understanding of what is and is not being accomplished in hypothesis testing.
• Real-life applications of the Poisson, binomial, and hypergeometric distributions – Generate student interest using topics such as flaws in manufactured copper wire, highway potholes, hospital patient traffic, airport luggage screening, and homeland security.
• Expanded treatment of R2, the coefficient of determination – Centers around the need to compromise between achieving a “good fit” and the inevitable loss in error degrees of freedom when one “overfits.”
• Expandeddiscussion of Tukey’s test on multiple comparisons – Presents additional detail on the notion of error rate and a-values in the context of simultaneous confidence intervals.
• New section on data transformation in analysis of variance – Addresses the robustness of analysis of variance to the assumption of homogeneous variance, connecting the discussion to previous sections on diagnostic plots to detect violations in assumptions.
• Interaction plots – Provides examples of scientific interpretations and new exercises using graphics.
• New examples requiring the use of one-sided intervals – Includes confidence intervals, prediction intervals, and tolerance intervals.
• Two level designs as screening experiments — Highlights their role as part of a sequential plan in which the scientist or engineer attempts to learn about the process, assess the role of the candidate factors, and give insight to determine the best region of experimentation.
• New end-of-chapter review material – Emphasizes key ideas as well as the risks and hazards associated with practical application of the material.
• New self-contained chapter on Bayesian statistics – Takes a practical approach by introducing applications in many fields. Introduces the concept of subjective probability, and treats point and interval estimation from a Bayesian point of view using practical examples.
• Extensive updates throughout – Includes new material on topics such as: difference between discrete and continuous measurements; binary data; quartiles; importance of experimental design; “dummy” variables; rules for expectations and variances of linear functions; Poisson distribution; Weibull and lognormal distributions; central limit theorem, and data plotting.
• New exercises on random variables and probability distributions – Use compelling topics such as particle size distribution for missile fuel; measurement errors in scientific systems; studies of time to failure for washing machines; assembly-line production of electron tubes; product shelf life; airport passenger congestion; chemical impurities; failure in systems of electronic components working in parallel, and many others.
• Restructured coverage of hypothesis testing – Helps students develop a clear understanding of what is and is not being accomplished in hypothesis testing.
• Real-life applications of the Poisson, binomial, and hypergeometric distributions – Generate student interest using topics such as flaws in manufactured copper wire, highway potholes, hospital patient traffic, airport luggage screening, and homeland security.
• Expanded treatment of R2, the coefficient of determination – Centers around the need to compromise between achieving a “good fit” and the inevitable loss in error degrees of freedom when one “overfits.”
• Expandeddiscussion of Tukey’s test on multiple comparisons – Presents additional detail on the notion of error rate and a-values in the context of simultaneous confidence intervals.
• New section on data transformation in analysis of variance – Addresses the robustness of analysis of variance to the assumption of homogeneous variance, connecting the discussion to previous sections on diagnostic plots to detect violations in assumptions.
• Interaction plots – Provides examples of scientific interpretations and new exercises using graphics.
• New examples requiring the use of one-sided intervals – Includes confidence intervals, prediction intervals, and tolerance intervals.
• Two level designs as screening experiments — Highlights their role as part of a sequential plan in which the scientist or engineer attempts to learn about the process, assess the role of the candidate factors, and give insight to determine the best region of experimentation.