 |
 |
 |
 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 |
Essentials of Probability & Statistics for Engineers & Scientists+作者：
Walpole/Myers/Myers/Ye+年份：
2013 年1 版
+ISBN：
9780321845849
+書號：
PS0387PC
+規格：
平裝/套色
+頁數：
480
+出版商：
Pearson(Asia)
+參考資訊：
|
定價
$ |
|
本站購物功能已關閉，點選"購物車"圖示會自動連結到新的購書網頁！或與LINE客服諮詢聯繫
讀者購書請至★滄海書局‧鼎隆圖書購書網 ★https://eshop.tsanghai.com.tw/★
滄海ESHOP購書網提供更方便、快速訂購、結帳付款的購書服務，並提供數位產品購買專區～
書籍若有教學輔助配件，僅提供採用老師教學使用，是非賣品，不販售，亦無法提供一般讀者。
•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.
?Case studies provide deeper insight into the practicality of the concepts.
?Calculus is confined to elementary probability theory and probability distributions (Chapters1–3).
?Linear algebra and the use of matrices are applied only in Section 7.11, where treatment of multiple linear regression and analysis of variance is covered.
•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.
?Class projects 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.
•Statistical software coverage in the following case studies includes SAS® and MINITAB®, 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.
•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 common pitfalls in misusing statistics.
•Topic outline
?Chapter 1: elementary overview of statistical inference and basic probability
?Chapter 2: random variables, probability distributions, and expectations
?Chapter 3: specific discrete and continuous distributions with illustrations of their use and relationships among them
?Chapter 4: materials on graphical methods; an important introduction to the notion of sampling distribution
?Chapters 5–6: one- and two- sample point and interval estimation, statistical hypothesis testing
?Chapters 7–9: simple and multiple linear regressions; analysis of variance; multi-factorial experiments
?Case studies provide deeper insight into the practicality of the concepts.
?Calculus is confined to elementary probability theory and probability distributions (Chapters1–3).
?Linear algebra and the use of matrices are applied only in Section 7.11, where treatment of multiple linear regression and analysis of variance is covered.
•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.
?Class projects 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.
•Statistical software coverage in the following case studies includes SAS® and MINITAB®, 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.
•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 common pitfalls in misusing statistics.
•Topic outline
?Chapter 1: elementary overview of statistical inference and basic probability
?Chapter 2: random variables, probability distributions, and expectations
?Chapter 3: specific discrete and continuous distributions with illustrations of their use and relationships among them
?Chapter 4: materials on graphical methods; an important introduction to the notion of sampling distribution
?Chapters 5–6: one- and two- sample point and interval estimation, statistical hypothesis testing
?Chapters 7–9: simple and multiple linear regressions; analysis of variance; multi-factorial experiments
Ronald E. Walpole - Roanoke College
Raymond Myers - Virginia Tech
Sharon L. Myers - Radford University
Keying E. Ye - University of Texas at San Antonio
Raymond Myers - Virginia Tech
Sharon L. Myers - Radford University
Keying E. Ye - University of Texas at San Antonio
1. Introduction to Statistics and Probability
2. Random Variables, Distributions, and Expectations
3. Some Probability Distributions
4. Sampling Distributions and Data Descriptions
5. One- and Two-Sample Estimation Problems
6. One- and Two-Sample Tests of Hypotheses.
7. Linear Regression
8. One-Factor Experiments: General
9. Factorial Experiments (Two or More Factors)
2. Random Variables, Distributions, and Expectations
3. Some Probability Distributions
4. Sampling Distributions and Data Descriptions
5. One- and Two-Sample Estimation Problems
6. One- and Two-Sample Tests of Hypotheses.
7. Linear Regression
8. One-Factor Experiments: General
9. Factorial Experiments (Two or More Factors)
