William Navidi is Professor of Applied Mathematics and Statistics at the Colorado School of Mines.He received his B.A. degree in mathematics from New College， his M.A. in mathematics from Michigan State University，and his Ph.D. in statistics from the University of California at Berkeley. Professor Navidi has authored more than 70 research papers both in statistical theory and in a wide variety of applications includ-ing computer networks， epidemiology， molecular biology， chemical engineering， and geophysics.
目錄：
Preface
Acknowledgments of Reviewers and Contributors
Key Features
Supplements for Students and Instructors
Chapter 1 Sampling and Descriptive Statistics Introduction
1．1 Sampling
1．2 Summary Statistics
1．3 Graphical Summaries
Chapter 2 Probability
Introduction
2．1 Basicldeas
2．2 Counting Methods
2．3 Conditional Probability and Independence
2．4 Random Variables
2．5 Linear Functions of Random Variables
2．6 Jointly Distributed Random Variables
Chapter 3 Propagation of Error Introduction
3．1 Measurement Error
3．2 Linear Combinations of Measurements
3．3 Uncertainties for Functions of One Measurement
3．4 Uncertainties for Functions of Several Measurements
Chapter 4 Commonly Used Distributions
Introduction
4．1 The Bernoulli Distribution
4，2 The Binomial Distribution
4，3 The Poisson Distribution
4．4 Some Other Discrete Distributions
4．5 The Normal Distribution
4．6 The Lognormal Distribution
4．7 The Exponential Distribution
4．8 Some Other Continuous Distributions
4．9 Some Principles of Point Estimation
4．10 Probability Plots
4．11 The Central Limit Theorem
4．12 Simulation
Chapter 5 Confidencelntervals Introduction
5．1 Large-Sample Confidence Intervals
for a Population Mean
5．2 Confidence Intervals for Proportions
5．3 Small-Sample Confidence Intervals for a Population Mean
Chapter 6 Hypothesis Testing
Chapter 7 Correlation and Simple Linear Regression
Chapter 8 Multiple Regression
Chapter 9 Factorial Experiments
Chapter 10 Statistical Quality Control
Appendix A：Tables 804．
Appendix B： PartiaI Derivatives
Appendix C： Bibliography
Answers to Odd-Numbered Exercises
Index
编辑手记
內容試閱：
The idea for this book grew out of discussions between the statistics faculty and the engineering faculty at the Colorado School of Mines regarding our introductory statis-tics course for engineers. Our engineering faculty felt that the students needed sub-stantial coverage of propagation of error， as well as more emphasis on model-fitting skills. The statistics faculty believed that students needed to become more aware of some important practical statistical issues such as the checking of model assumptions and the use of simulation.
My view is that an introductory statistics text for students in engineering and sci-ence should offer all these topics in some depth. In addition， it should be flexible enough to allow for a variety of choices to be made regarding coverage， because there are many different ways to design a successful introductory statistics course. Finally，it should provide examples that present important ideas in realistic settings. Accord-ingly， the book has the following features：The book is flexible in its presentation of probability， allowing instructors wide lat-itude in choosing the depth and extent of their coverage of this topic.The book contains many examples that feature real， contemporary data sets， both to motivate students and to show connections to industry and scientific research.The book contains many examples of computer output and exercises suitable for solving with computer software.
The book provides extensive coverage of propagation of error.
The book presents a solid introduction to simulation methods and the bootstrap，including applications to verifying normality assumptions， computing probabilities，estimating bias， computing confidence intervals， and testing hypotheses.
The book provides more extensive coverage of linear model diagnostic procedures than is found in most introductory texts. This includes material on examination of residual plots， transformations of variables， and principles of variable selection in multivariate models.
The book covers the standard introductory topics， including descriptive statistics，probability， confidence intervals， hypothesis tests， linear regression， factorial experiments， and statistical quality control.
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