基本信息
- 原书名：Essentials of Probability & Statistics for Engineers & Scientists
- 作者： (美)Ronald E.Walpole Raymond H.Myers Sharon L.Myers Keying Ye
- 丛书名： 华章统计学原版精品系列
- 出版社：机械工业出版社
- ISBN：9787111425069
- 上架时间：2013-5-29
- 出版日期：2013 年6月
- 开本：16开
- 页码：467
- 版次：1-1
- 所属分类：数学 > 概率论与数理统计 > 概率统计
编辑推荐
<p>《概率与统计(英文版)/华章统计学原版精品系列》编著者沃波尔
本书是数理统计学的优秀入门教材，深入浅出地介绍了统计理论与方法。通过大量的实际数据例子强调概率模型和统计方法的应用，使读者更能洞悉和体会统计思维与统计方法的本质。本书主要内容包括：统计与概率导论，随机变量、分布和数学期望，概率分布，抽样分布和数据描述，单样本和两样本的估计问题。单样本和两样本的假设检验，线性回归，析因试验等。</p>
内容简介
目录
《概率与统计(英文版)》
Preface
1 Introduction to Statistics and Probability
1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability
1.2 Sampling Procedures; Collection of Data
1.3 Discrete and Continuous Data
1.4 Probability: Sample Space and Events
Exercises
1.5 Counting Sample Points
Exercises
1.6 Probability of an Event
1.7 Additive Rules
Exercises
1.8 Conditional Probability, Independence, and the Product Rule
Exercises
1.9 Bayes' Rule
Exercises
ReviewExercises
2 Random Variables, Distributions,and Expectations
2.1 Concept of a Random Variable
Preface
1 Introduction to Statistics and Probability
1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability
1.2 Sampling Procedures; Collection of Data
1.3 Discrete and Continuous Data
1.4 Probability: Sample Space and Events
Exercises
1.5 Counting Sample Points
Exercises
1.6 Probability of an Event
1.7 Additive Rules
Exercises
1.8 Conditional Probability, Independence, and the Product Rule
Exercises
1.9 Bayes' Rule
Exercises
ReviewExercises
2 Random Variables, Distributions,and Expectations
2.1 Concept of a Random Variable
前言
General Approach and Mathematical Level
This text was designed for a one-semester course that covers the essential topics needed for a fundamental understanding of basic statistics and its applications in the fields of engineering and the sciences. A balance between theory and application is maintained throughout the text. Coverage of analytical tools in statistics is enhanced with the use of calculus when discussion centers on rules and concepts in probability. Students using this text should have the equivalent of the completion of one semester of differential and integral calculus. Linear algebra would be helpful but not necessary if the instructor chooses not to include Section 7.11 on multiple linear regression using matrix algebra.
Class projects and case studies are presented throughout the text to give the student a deeper understanding of real-world usage of statistics. Class projects provide the opportunity for students to work alone or in groups to gather their own experimental data and draw inferences using the data. In some cases, the work conducted by the student involves a problem whose solution will illustrate the meaning of a concept and/or will provide an empirical understanding of an important statistical result. Case studies provide commentary to give the student a clear understanding in the context of a practical situation. The comments we affectionately call "Pot Holes" at the end of each chapter present the big pic- ture and show how the chapters relate to one another. They also provide warn- ings about the possible misuse of statistical techniques presented in the chapter. A large number of exercises are available to challenge the student. These exer- cises deal with real-life scientific and engineering applications. The many data sets associated with the exercises are available for download from the website ttp://www.pearsonhighered.com/mathstatsresources.
Content and Course Planning
This textbook contains nine chapters. The first two chapters introduce the notion of random variables and their properties, including their role in characterizing data sets. Fundamental to this discussion is the distinction, in a practical sense, between populations and samples.
In Chapter 3, both discrete and continuous random variables are illustrated with examples. The binomial, Poisson, hypergeometric, and other useful discrete distributions are discussed. In addition, continuous distributions include the nor-mai, gamma, and exponential. In all cases, real-life scenarios are given to reveal how these distributions are used in practical engineering problems.
The material on specific distributions in Chapter 3 is followed in Chapter 4 by practical topics such as random sampling and the types of descriptive statistics that convey the center of location and variability of a sample. Examples involv-ing the sample mean and sample variance are included. Following the introduc-tion of central tendency and variability is a substantial amount of material dealing with the importance of sampling distributions. Real-life illustrations highlight how sampling distributions are used in basic statistical inference. Central Limit type methodology is accompanied by the mechanics and purpose behind the use of the normal, Student t, X2, and f distributions, as well as examples that illustrate their use. Students are exposed to methodology that will be brought out again in later chapters in the discussions of estimation and hypothesis testing. This fundamental methodology is accompanied by illustration of certain important graphical meth-ods, such as stem-and-leaf and box-and-whisker plots. Chapter 4 presents tile first of several case studies involving real data.
Chapters 5 and 6 complement each other, providing a foundation for the solu-tion of practical problems in which estimation and hypothesis testing are employed.Statistical inference involving a single mean and two means, as well as one and two proportions, is covered. Confidence intervals are displayed and thoroughly dis- cussed; prediction intervals and tolerance intervals are touched upon. Problems with paired observations are covered in detail.
In Chapter 7, the basics of simple linear regression (SLR) and multiple linear regression (MLR) are covered in a depth suitable for a one-semester course. Chap-ters 8 and 9 use a similar approach to expose students to the standard methodology associated with analysis of variance (ANOVA). Although-regression and ANOVA are challenging topics, the clarity of presentation, along with case studies, class projects, examples, and exercises, allows students to gain an understanding of the essentials of both.
In the discussion of rules and concepts in probability, the coverage of analytical tools is enhanced through the use of calculus. Though the material on multiple linear regression in Chapter 7 covers the essential methodology, students are not burdened with the level of matrix algebra and relevant manipulations that they would confront in a text designed for a two-semester course.
Computer Software
Case studies, beginning in Chapter 4, feature computer printout and graphical material generated using both SAS and MINITAB. The inclusion of the com-puter reflects our belief that students should have the experience of reading and interpreting computer printout and graphics, even if the software in the text is not that which is used by the instructor. Exposure to more than one type of software can broaden the experience base for the student. There is no reason to believe that the software used in the course will be that which the student will be called upon to use in a professional setting.
Supplements
Instructor's Solutions Manual. This resource contains worked-out solutions to all text exercises and is available for download from Pearson's Instructor Resource Center at www.pearsonhighered.com/irc.
Student's Solutions Manual. ISBN-10: 0-321-78399-9; ISBN-13: 978-0-321-78399-8. This resource contains complete solutions to selected exercises. It is available for purchase from MyPearsonStore at www.mypearsonstore.com, or ask your local representative for value pack options.
PowerPoint Lecture Slides. These slides include most of the figures and tables from the text. Slides are available for download from Pearson's Instructor Resource Center at www.pearsonhighered.com/irc.
Looking for more comprehensive coverage for a two-semester course? See the more comprehensive book Probability and Statistics for Engineers and Scientists, 9th edition, by Walpole, Myers, Myers, and Ye (ISBN-10: 0-321-62911-6; ISBN-13:978-0-321-62911-1).
Acknowledgment s
We are indebted to those colleagues who provided many helpful suggestions for this text. They are David Groggel, Miami University; Lance Hemlow, Raritan Valley Community College; Ying Ji, University of Texas at San Antonio; Thomas Kline,University of Northern Iowa; Sheila Lawrence, Rutgers University; Luis Moreno,Broome County Community College; Donald Waldman, University of Colorado--Boulder;, and Marlene Will, Spalding University. We would also like to thank Delray Schultz, Millersville University, and Keith Friedman, University of Texas -- Austin, for ensuring the accuracy of this text.
We would like to thank the editorial and production services provided by nu-merous people from Pearson/Prentice Hall, especially the editor in chief Deirdre Lynch, acquisitions editor Chris Cummings, sponsoring editor Christina Lepre, as-sociate content editor Dana Bettez, editorial assistant Sonia Ashraf, production project manager Tracy Patruno, and copyeditor Sally Lifiand. We thank the Vir-ginia Tech Statistical Consulting Center, which was the source of many real-life data sets.
This text was designed for a one-semester course that covers the essential topics needed for a fundamental understanding of basic statistics and its applications in the fields of engineering and the sciences. A balance between theory and application is maintained throughout the text. Coverage of analytical tools in statistics is enhanced with the use of calculus when discussion centers on rules and concepts in probability. Students using this text should have the equivalent of the completion of one semester of differential and integral calculus. Linear algebra would be helpful but not necessary if the instructor chooses not to include Section 7.11 on multiple linear regression using matrix algebra.
Class projects and case studies are presented throughout the text to give the student a deeper understanding of real-world usage of statistics. Class projects provide the opportunity for students to work alone or in groups to gather their own experimental data and draw inferences using the data. In some cases, the work conducted by the student involves a problem whose solution will illustrate the meaning of a concept and/or will provide an empirical understanding of an important statistical result. Case studies provide commentary to give the student a clear understanding in the context of a practical situation. The comments we affectionately call "Pot Holes" at the end of each chapter present the big pic- ture and show how the chapters relate to one another. They also provide warn- ings about the possible misuse of statistical techniques presented in the chapter. A large number of exercises are available to challenge the student. These exer- cises deal with real-life scientific and engineering applications. The many data sets associated with the exercises are available for download from the website ttp://www.pearsonhighered.com/mathstatsresources.
Content and Course Planning
This textbook contains nine chapters. The first two chapters introduce the notion of random variables and their properties, including their role in characterizing data sets. Fundamental to this discussion is the distinction, in a practical sense, between populations and samples.
In Chapter 3, both discrete and continuous random variables are illustrated with examples. The binomial, Poisson, hypergeometric, and other useful discrete distributions are discussed. In addition, continuous distributions include the nor-mai, gamma, and exponential. In all cases, real-life scenarios are given to reveal how these distributions are used in practical engineering problems.
The material on specific distributions in Chapter 3 is followed in Chapter 4 by practical topics such as random sampling and the types of descriptive statistics that convey the center of location and variability of a sample. Examples involv-ing the sample mean and sample variance are included. Following the introduc-tion of central tendency and variability is a substantial amount of material dealing with the importance of sampling distributions. Real-life illustrations highlight how sampling distributions are used in basic statistical inference. Central Limit type methodology is accompanied by the mechanics and purpose behind the use of the normal, Student t, X2, and f distributions, as well as examples that illustrate their use. Students are exposed to methodology that will be brought out again in later chapters in the discussions of estimation and hypothesis testing. This fundamental methodology is accompanied by illustration of certain important graphical meth-ods, such as stem-and-leaf and box-and-whisker plots. Chapter 4 presents tile first of several case studies involving real data.
Chapters 5 and 6 complement each other, providing a foundation for the solu-tion of practical problems in which estimation and hypothesis testing are employed.Statistical inference involving a single mean and two means, as well as one and two proportions, is covered. Confidence intervals are displayed and thoroughly dis- cussed; prediction intervals and tolerance intervals are touched upon. Problems with paired observations are covered in detail.
In Chapter 7, the basics of simple linear regression (SLR) and multiple linear regression (MLR) are covered in a depth suitable for a one-semester course. Chap-ters 8 and 9 use a similar approach to expose students to the standard methodology associated with analysis of variance (ANOVA). Although-regression and ANOVA are challenging topics, the clarity of presentation, along with case studies, class projects, examples, and exercises, allows students to gain an understanding of the essentials of both.
In the discussion of rules and concepts in probability, the coverage of analytical tools is enhanced through the use of calculus. Though the material on multiple linear regression in Chapter 7 covers the essential methodology, students are not burdened with the level of matrix algebra and relevant manipulations that they would confront in a text designed for a two-semester course.
Computer Software
Case studies, beginning in Chapter 4, feature computer printout and graphical material generated using both SAS and MINITAB. The inclusion of the com-puter reflects our belief that students should have the experience of reading and interpreting computer printout and graphics, even if the software in the text is not that which is used by the instructor. Exposure to more than one type of software can broaden the experience base for the student. There is no reason to believe that the software used in the course will be that which the student will be called upon to use in a professional setting.
Supplements
Instructor's Solutions Manual. This resource contains worked-out solutions to all text exercises and is available for download from Pearson's Instructor Resource Center at www.pearsonhighered.com/irc.
Student's Solutions Manual. ISBN-10: 0-321-78399-9; ISBN-13: 978-0-321-78399-8. This resource contains complete solutions to selected exercises. It is available for purchase from MyPearsonStore at www.mypearsonstore.com, or ask your local representative for value pack options.
PowerPoint Lecture Slides. These slides include most of the figures and tables from the text. Slides are available for download from Pearson's Instructor Resource Center at www.pearsonhighered.com/irc.
Looking for more comprehensive coverage for a two-semester course? See the more comprehensive book Probability and Statistics for Engineers and Scientists, 9th edition, by Walpole, Myers, Myers, and Ye (ISBN-10: 0-321-62911-6; ISBN-13:978-0-321-62911-1).
Acknowledgment s
We are indebted to those colleagues who provided many helpful suggestions for this text. They are David Groggel, Miami University; Lance Hemlow, Raritan Valley Community College; Ying Ji, University of Texas at San Antonio; Thomas Kline,University of Northern Iowa; Sheila Lawrence, Rutgers University; Luis Moreno,Broome County Community College; Donald Waldman, University of Colorado--Boulder;, and Marlene Will, Spalding University. We would also like to thank Delray Schultz, Millersville University, and Keith Friedman, University of Texas -- Austin, for ensuring the accuracy of this text.
We would like to thank the editorial and production services provided by nu-merous people from Pearson/Prentice Hall, especially the editor in chief Deirdre Lynch, acquisitions editor Chris Cummings, sponsoring editor Christina Lepre, as-sociate content editor Dana Bettez, editorial assistant Sonia Ashraf, production project manager Tracy Patruno, and copyeditor Sally Lifiand. We thank the Vir-ginia Tech Statistical Consulting Center, which was the source of many real-life data sets.
