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|1||Introduction to Statistics and Data Analysis||1|
|3||Random Variables and Probability Distributions||51|
|5||Some Discrete Probability Distributions||114|
|6||Some Continuous Probability Distributions||143|
|7||Functions of Random Variables||180|
|8||Random Sampling, Data Description, and Some Fundamental Sampling Distributions||198|
|9||One- and Two-Sample Estimation Problems||238|
|10||One- and Two-Sample Tests of Hypotheses||290|
|11||Simple Linear Regression and Correlation||358|
|12||Multiple Linear Regression||405|
|13||One-Factor Experiments: General||461|
|15||2[superscript k] Factorial Experiments and Fractions||559|
|17||Statistical Quality Control||635|
|Appendix: Statistical Tables||671|
|Answers to Odd-Numbered Exercises||723|
The seventh edition emphasizes and illustrates the use of probabilistic models and statistical methodology that is employed in countless applications in all areas of science and engineering. There remains an important balance between theory and methodology that is featured in the text. We do not avoid the use of some theory but our goal is to let the mathematics provide insight rather than be a distraction. We feel that engineers and scientists are trained in mathematics and thus the providing of mathematical support when needed keeps the pedagogy from becoming a series of illustrated recipes in which the concepts are not understood and could never be applied or extended by the student except within very narrow bounds.
The text contains an abundance of exercises in which the methodology discussed is illustrated by the use of real-life scientific scenarios and data sets. The complete set of data files which accompany the text are available for download from the text companion website, located at our site. Though we attempt to appeal to engineers, the exercises are not confined to engineering applications. The student is exposed to problems encountered in many sciences including social sciences and biomedical applications. The motivation here stems from the fact that trained engineers are more and more becoming exposed to nontraditional settings, including areas like bioinformatics and bioengineering.
While we do let calculus play an important role but it should be noted that its use is confined to elementary probability theory and properties of probability distributions (Chapters 3, 4, 6, and 7). In addition, a modest amount ofmatrix algebra is used to support the linear regression material in Chapters 11 and 12. This is despite the fact that an "optional" section appears in Chapter 11 that includes the development of the multiple linear regression model with more substantive use of matrices. The student who uses this text should have completed one semester or two quarters of differential and integral calculus. An exposure to matrix algebra would be helpful but not necessary if the course content excludes the aforementioned optional section.
Chapters 11-17 contain ample material for a second semester of a two-semester course. Chapters 11 and 12 cover simple and multiple linear regression respectively. However, Chapter 12 contains new material that deals with special nonlinear models involved when one deals with nonnormal responses. As a result, logistic and Poisson regression are treated along with important practical illustrations. This in addition to new material in categorical variable regression again provides considerable flexibility for the instructor in his or her treatment of regression. The treatment of regression in this text is extensive and many special regression topics in Chapter 12 are self-contained. Chapters 13 through 17 contain topics in analysis of variance, design of experiments, nonparametric statistics, and quality control.
As in previous editions there are many case studies that demonstrate statistical analysis of interesting real-life data sets. In most cases graphical techniques are used. These case studies are featured in two sample hypothesis testing, multiple linear regression, analysis of variance, and the analysis of 2-level experimental designs. Where appropriate, the use of residual plots, quantile plots, and normal probability plots are described in the analysis. Computer output is used for illustration purposes for these case studies and for other examples in the text. In that regard both SAS and MINITAB are featured. We have always felt that the experience of reading computer printout is invaluable to the student even if the package or packages featured in the text are not what is used by the instructor. Exposure to more than one type of software can broaden the experience base for the student. There is certainly no reason to believe that the software in the course is that which he or she will be called upon to use in practice.
We are indeed indebted to those colleagues who reviewed the sixth edition and provided many helpful suggestions for this edition. They are: Ruxu Du, University of Miami; Nirmal Devi, Embry Riddle; Judith Miller, Georgetown University; Stephanie Edwards, Bemidji State University. We would like to thank personnel at the Virginia Tech Statistical Consulting Center. The consulting center was the source of many real-life data sets. In addition we thank Linda Seawell who worked hard in the typing and preparation of the manuscript.
Posted September 12, 2003
I found this a good reference book for working with operations research models. Very useful with subset analysis and other basic analysis for regression models. This is a standard in the classes I have attended. Could be better for beginning students. Great for advanced students.Was this review helpful? Yes NoThank you for your feedback. Report this reviewThank you, this review has been flagged.
Posted March 30, 2003
The textbook is poor and the examples are lacking. I do not know how they have made it to the 7th edition. If they have made improvements with each edition the previous editions must have been horrible. Of course that is just my opinion I could be wrong.Was this review helpful? Yes NoThank you for your feedback. Report this reviewThank you, this review has been flagged.