Probability and Statistics for Engineers and Scientists 9/e (絕)
- 20本以上，享 8.5折
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- 一般書籍
- ISBN：9780321748232
- 作者：Walpole
- 版次：9
- 年份：2011
- 出版商：Pearson Education
書籍介紹
本書特色
目錄
Description
This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance between theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding.
This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance between theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding.
Features
New to This Edition
- The balance between theory and applications offers mathematical support to enhance coverage when necessary, giving engineers and scientists the proper mathematical context for statistical tools and methods.
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Mathematical level: this text assumes one semester of differential and integral calculus as a prerequisite.
- Calculus is confined to elementary probability theory and probability distributions (Chapters 2—7).
- Matrix algebra is used modestly in coverage of linear regression material (Chapters 11—12).
- Linear algebra and the use of matrices are applied in Chapters 11—15, where treatment of linear regression and analysis of variance is covered.
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Compelling exercise sets challenge students to use the concepts to solve problems that occur in many real-life scientific and engineering situations. Many exercises contain real data from studies in the fields of biomedical, bioengineering, business, computing, etc.
＊ 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. -
Statistical software coverage in the following case studies includes SASR and MINITABR, with screenshots and graphics as appropriate:
- Two-sample hypothesis testing
- Multiple linear regression
- Analysis of variance
- Use of two-level factorial-experiments
- Interaction plots provide examples of scientific interpretations and new exercises using graphics.
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Topic outline
- Chapter 1: elementary overview of statistical inference
- Chapters 2—4: basic probability; discrete and continuous random variables
- Chapters 2—10: probability distributions and statistical inferences
- Chapters 5—6: specific discrete and continuous distributions with illustrations of their use and relationships among them
- Chapter 7: optional chapter covering the transformation of random variables.
- Chapter 8: additional materials on graphical methods; an important introduction to the notion of sampling distribution
- Chapters 9—10: one and two sample point and interval estimation
- Chapters 11—15: linear regression; analysis of variance
New to This Edition
- Revised text focuses on improved clarity and deeper understanding rather than adding extraneous new material.
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End-of-chapter material strengthens the connections between chapters.
- “Pot Holes” comments remind students of the bigger picture and how each chapter fits into that picture. These notes also discuss limitations of specific procedures and help students avoid pitfalls in misusing statistics.
- Class projects in several chapters provide the opportunity for students to gather their own experimental data and draw inferences from that data. These projects illustrate the meaning of a concept or provide empirical understanding of important statistical results, and are suitable for either group or individual work.
- Case studies provide deeper insight into the practicality of the concepts.
Table of Contents
1. Introduction to Statistics and Data Analysis
2. Probability
3. Random Variables and Probability Distributions
4. Mathematical Expectation
5. Some Discrete Probability Distributions
6. Some Continuous Probability Distributions
7. Functions of Random Variables (Optional)
8. Fundamental Sampling Distributions and Data Descriptions
9. One- and Two-Sample Estimation Problems
10. One- and Two-Sample Tests of Hypotheses
11. Simple Linear Regression and Correlation
12. Multiple Linear Regression and Certain Nonlinear Regression Models
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
1. Introduction to Statistics and Data Analysis
2. Probability
3. Random Variables and Probability Distributions
4. Mathematical Expectation
5. Some Discrete Probability Distributions
6. Some Continuous Probability Distributions
7. Functions of Random Variables (Optional)
8. Fundamental Sampling Distributions and Data Descriptions
9. One- and Two-Sample Estimation Problems
10. One- and Two-Sample Tests of Hypotheses
11. Simple Linear Regression and Correlation
12. Multiple Linear Regression and Certain Nonlinear Regression Models
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
