Statistics for Engineers and Scientists 3/e (絕)
- 20本以上，享 8.5折
售價
$
洽詢
- 一般書籍
- ISBN：9780071222051
- 作者：William Navidi
- 版次：3
- 年份：2011
- 出版商：McGraw-Hill
書籍介紹
本書特色
目錄
作者介紹
Description
Statistics for Engineers and Scientists stands out for its crystal clear presentation of applied statistics. Suitable for a one or two semester course, the book takes a practical approach to methods of statistical modeling and data analysis that are most often used in scientific work.
Statistics for Engineers and Scientists features a unique approach highlighted by an engaging writing style that explains difficult concepts clearly, along with the use of contemporary real world data sets to help motivate students and show direct connections to industry and research. While focusing on practical applications of statistics, the text makes extensive use of examples to motivate fundamental concepts and to develop intuition.
Statistics for Engineers and Scientists stands out for its crystal clear presentation of applied statistics. Suitable for a one or two semester course, the book takes a practical approach to methods of statistical modeling and data analysis that are most often used in scientific work.
Statistics for Engineers and Scientists features a unique approach highlighted by an engaging writing style that explains difficult concepts clearly, along with the use of contemporary real world data sets to help motivate students and show direct connections to industry and research. While focusing on practical applications of statistics, the text makes extensive use of examples to motivate fundamental concepts and to develop intuition.
Features
- Over 250 new problems have been added
- A new section was added on Tolerance and Prediction Intervals in Chapter 5; the discussion of controlled experiments and observational studies was added to Chapter 1; and confounding in controlled experiments was added in Chapter 7.
- Flexible presentation of probability addresses the needs of different courses. Allowing for a mathematically rigorous approach, the major results are derived from axioms, with proofs given for most of them. Each result is illustrated with an example or two to promote intuitive understanding. Instructors who prefer a more informal approach may therefore focus on the examples rather than the proofs and skip the optional sections.
- An engaging writing style explains difficult concepts clearly. While including the mathematics necessary for clear understanding, the text makes extensive use of examples to motivate fundamental concepts and to develop intuition.
- Contemporary, real world data sets are one of the defining features of this text. With a fresh approach to the subject, the author uses contemporary data sets to help motivate students and show direct connection to industry and research.
- In line with modern trends, the text includes numerous examples of computer output and contains exercises suitable for solving with computer software. These examples and exercises involve interpreting, as well as generating, computer output. The student edition of MINITAB, the widely used statistical software package, is available bundled with the text.
- A separate chapter provides extensive coverage of propagation of error, sometimes called "error analysis" or the "delta method." The coverage is more extensive than in most texts, with a flexible format allowing instructors to easily cover selected topics.
- The text presents an extensive, self-contained introduction to simulation methods at a level appropriate for introductory students, including the bootstrap and applications to estimating probabilities, estimating bias, computing confidence intervals, and testing hypotheses.
- The text provides more extensive coverage of linear model diagnostic procedures than is found in most competing texts including a lengthy section on checking model assumptions and transforming variables. The coverage emphasizes that linear models are appropriate only when the relationship between variables is linear. This point is all the more important since it is often overlooked in practice by engineers and scientists (not to mention statisticians).
Table of Contents
Chapter 1: Sampling and Descriptive Statistics
Chapter 2: Probability
Chapter 3: Propagation of Error
Chapter 4: Commonly Used Distributions
Chapter 5: Confidence Intervals
Chapter 6: Hypothesis Testing
Chapter 7: Correlation and Simple Linear Regression
Chapter 8: Multiple Regression
Chapter 9: Factorial Experiments
Chapter 10: Statistical Quality Control
Chapter 1: Sampling and Descriptive Statistics
Chapter 2: Probability
Chapter 3: Propagation of Error
Chapter 4: Commonly Used Distributions
Chapter 5: Confidence Intervals
Chapter 6: Hypothesis Testing
Chapter 7: Correlation and Simple Linear Regression
Chapter 8: Multiple Regression
Chapter 9: Factorial Experiments
Chapter 10: Statistical Quality Control
William Navidi is Professor of Mathematical and Computer Sciences at the Colorado School of Mines. He received the B.A. degree in mathematics from New College, the M.A. in mathematics from Michigan State University, and the Ph.D. in statistics from the University of California at Berkeley. Professor Navidi has authored more than 50 research papers both in statistical theory and in a wide variety of applications including computer networks, epidemiology, molecular biology, chemical engineering, and geophysics.
