Fundamentals of Probability and Statistics for Engineers 工程师用概率与统计学基础
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作者： T. T. Soong 著
出 版 社：
出版时间： 2004-4-1字数：版次： 1页数： 391印刷时间： 2004/04/01开本： 16开印次： 1纸张： 胶版纸I S B N ： 9780470868140包装： 平装内容简介
This textbook differs from others in the field in that it has been prepared very much with students and their needs in mind, having been classroom tested over many years. It is a true “learner’s book” made for students who require a deeper understanding of probability and statistics. It presents the fundamentals of the subject along with concepts of probabilistic modelling, and the process of model selection, verification and analysis. Furthermore, the inclusion of more than 100 examples and 200 exercises (carefully selected from a wide range of topics), along with a solutions manual for instructors, means that this text is of real value to students and lecturers across a range of engineering disciplines.
Key features:
Presents the fundamentals in probability and statistics along with relevant applications.
Explains the concept of probabilistic modelling and the process of model selection, verification and analysis.
Definitions and theorems are carefully stated and topics rigorously treated.
Includes a chapter on regression analysis.
Covers design of experiments.
Demonstrates practical problem solving throughout the book with numerous examples and exercises purposely selected from a variety of engineering fields.
Includes an accompanying online Solutions Manual for instructors containing complete step-by-step solutions to all problems.
Download Description
"This textbook differs from others in the field in that it has been prepared very much with students and their needs in mind, having been classroom tested over many years. It is a true “learner’s book” made for students who require a deeper understanding of probability and statistics. It presents the fundamentals of the subject along with concepts of probabilistic modelling, and the process of model selection, verification and analysis. Furthermore, the inclusion of more than 100 examples and 200 exercises (carefully selected from a wide range of topics), along with a solutions manual for instructors, means that this text is of real value to students and lecturers across a range of engineering disciplines.
Key features:
Presents the fundamentals in probability and statistics along with relevant applications.
Explains the concept of probabilistic modelling and the process of model selection, verification and analysis.
Definitions and theorems are carefully stated and topics rigorously treated.
Includes a chapter on regression analysis.
Covers design of experiments.
Demonstrates practical problem solving throughout the book with numerous examples and exercises purposely selected from a variety of engineering fields.
Includes an accompanying online Solutions Manual for instructors containing complete step-by-step solutions to all problems.
目录
Preface
1 Introduction
1.1 Organization of Text
1.2 Probability Tables and Computer Software
1.3 Prerequisites
Part A: Probability and Random Variables
2 Basic Probability Concepts
2.1 Elements of Set Theory
2.2 Sample Space and Probability Measure
2.3 Statistical Independence
2.4 Conditional Probability
Reference
Further Reading
Problems
3 Random Variables and Probability Distributions
3.1 Random Variables
3.2 Probability Distributions
3.3 Two or More Random Variables
3.4 Conditional Distribution and independence
Further Reading and Comments
Problems
4 Expectations And Moments
4.1 Moments of a Single Random Variable
4.2 Chebyshev Inequality
4.3 Moments of Two or More Random Variables
4.4 Moments of Sums of Random Variables
4.5 Characteristic Functions
Further Reading and Comments
Problems
5 Functions of Random Variables
5.1 Functions of One Random Variable
5.2 Functions of Two or More Radom Variables
5.3 m Functions of n Random Variables
Reference
Problems
6 Some Important Discrete Distributions
6.1 Bernoulli Trials
……
7 Some Important Continuous Distributions
Part B: Statistical Inference, Parameter Estimation, and Model Verification
8 Observed Data and Graphical Representation
9 Parameter Estimation
10 Model Verification
11 Linear Models and Linear Regression
Appendix A: Tables
Appendix B: Computer Software
Appendix C: Answers to Selected Problems
Subject Index
