# Statistics for Engineering and the Sciences, Sixth Edition

## Book Preface

**Overview**

This text is designed for a two-semester introductory course in statistics for students majoring in engineering or any of the physical sciences. Inevitably, once these student graduate and are employed, they will be involved in the collection and analysis of data and will be required to think critically about the results. Consequently, they need to acquire knowledge of the basic concepts of data description and statistical inference and familiarity with statistical methods that will be required use on the job.

**Pedagogy**

Chapters 1 through 6 identify the objectives of statistics, explain how we can describe data, and present the basic concepts of probability. Chapters 7 and 8 introduce the two methods for making inferences about population parameters: estimation with confidence intervals and hypothesis testing. These notions are extended in the remaining chapters to cover other topics that are useful in analyzing engineering and scientific data, including the analysis of categorical data (Chapter 9), regression analysis and model building (Chapters 10–12), the analysis of variance for designed experiments (Chapters 13–14), nonparametric statistics ((Chapter 15), statistical quality control (Chapter 16), and product and system reliability (Chapter 17).

**Features**

Hallmark features of this text are as follows:

1. Blend of theory and applications. The basic theoretical concepts of mathematical statistics are integrated with a two-semester presentation of statistical methodology. Thus, the instructor has the option of presenting a course with either of two characteristics—a course stressing basic concepts and applied statistics, or a course that, while still tilted toward application, presents a modest introduction to the theory underlying statistical inference.

2. Statistical software applications with tutorials. The instructor and student have the option of using statistical software to perform the statistical calculations required. Output from three popular statistical software products — SAS, SPSS, and MINITAB—as well as Microsoft Excel are fully integrated into the text. Tutorials with menu screens and dialog boxes associated with the software are provided in Appendices C, D, and E. These tutorials are designed for the novice user; no prior experience with the software is needed.

3. Blended coverage of topics and applications. To meet the diverse needs of future engineers and scientists, the text provides coverage of a wide range of data analysis topics. The material on multiple regression and model building (Chapters 11–12), principles of experimental design (Chapter 13), quality control (Chapter 15), and reliability (Chapter 17) sets the text apart from the typical introductory statistics text. Although the material often refers to theoretical concepts, the presentation is oriented toward applications.

4. Real data-based examples and exercises. The text contains large number of applied examples and exercises designed to motivate students and suggest future uses of the methodology. Nearly every exercise and example is based on data or experimental results from actual engineering and scientific studies published in academic journals or obtained from the organization conducting the analysis. These applied exercises are located at the end of every section and at the ends of chapters.

5. Statistics in Action case studies. Each chapter begins with a discussion of an actual contemporary scientific study (“Statistics in Action”) and the accompanying data. The analysis and inferences derived from the study are presented at key points in the chapter (“Statistics in Action Revisited”). Our goal is to show the students the importance of applying sound statistical methods in order to evaluate the findings and to think through the statistical issues involved.

6. End-of-chapter summary material. At the end of each chapter, we provide a summary of the topics presented via a “Quick Review” (key words and key formulas), “Language Lab” (a listing of key symbols and pronunciation guide), and “Chapter Summary Notes/Guidelines”. These features help the student summarize and reinforce the important points from the chapter and are useful study tools.

7. Standard mathematical notation for a random variable. Throughout the chapters on random variables, we use standard mathematical notation for representing a random variable. Uppercase letters represent the random variable, and lowercase letters represent the values that the random variable can assume.

8. Bootstrapping and Bayesian methods. In optional sections, the text presents two alternative estimation methods (Section 7.12) and hypothesis testing methods (Section 8.13) that are becoming more popular in scientific studies—bootstrapping and Bayesian methods.

9. All data sets provided online. All of the data associated with examples, exercises, and Statistics in Action cases are made available online at www.crcpress.com/product/isbn/9781498728850. Each data file is marked with a icon and file name in the text. The data files are saved in four different formats: MINITAB, SAS, SPSS, and Excel. By analyzing these data using statistical software, calculations are minimized, allowing student to concentrate on the interpretation of the results.

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