PDF Download Applied Statistics for Engineers and Scientists: Using Microsoft Excel & Minitab By David M. Levine, Patricia P. Ramsey, Robert K. Smidt

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Applied Statistics for Engineers and Scientists: Using Microsoft Excel & Minitab By David M. Levine, Patricia P. Ramsey, Robert K. Smidt

PDF Download Applied Statistics for Engineers and Scientists: Using Microsoft Excel & Minitab By David M. Levine, Patricia P. Ramsey, Robert K. Smidt

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Sales Rank: #401051 in Books
Published on: 2000-09-18
Original language: English
Number of items: 1
Dimensions: 9.90" h x 1.60" w x 7.90" l, 3.42 pounds
Binding: Paperback
714 pages

From the Back Cover
This applied book for engineers and scientists, written in a non-theoretical manner, focuses on underlying principles that are important in a wide range of disciplines. It emphasizes the interpretation of results, the presentation and evaluation of assumptions, and the discussion of what should be done if the assumptions are violated. Integration of spreadsheet and statistical software complete this treatment of statistics. Chapter topics include describing and summarizing data; probability and discrete probability distributions; continuous probability distributions and sampling distributions; process control charts; estimation procedures; hypothesis testing; the design of experiments; and simple linear and multiple regression models. For individuals interested in learning statistics—without a high level of mathematical sophistication.

About the Author
David M. Levine is one of the world's leading innovators in statistics education. He is Professor Emeritus of Statistics and Computer Information Systems at Bernard M. Baruch College (CUNY), and co-author of several best-selling books, including "Statistics for Managers using Microsoft Excel, Quality Management," and "Six Sigma for Green Belts and Champions,"

Instructional designer David F. Stephan pioneered the classroom use of technology, and is a leader in making Excel more accessible to statistics students. He has co-authored several textbooks with David M. Levine.

Robert K. Smidt earned a B.S. in mathematics from Manhattan College, an M.S. in statistics from Rutgers University, and a Ph.D. in statistics from the University of Wyoming. Dr. Smidt has taught at the University of Florida, University of Wyoming, Rutgers University, Oregon State University, Cuesta College, Fashion Institute of Technology, for LaVerne College at Vandenberg AFB, for Chapman College at the California Mens Colony, and for the Continuing Education Institute. He is currently professor and former chair of the Statistics Department at Cal Poly, San Luis Obispo, California. Dr. Smidt has broad statistical consulting experience. He has worked for or with the Department of Defense at the Pentagon, Lawrence Livermore National Laboratory, Tenera, Diablo Canyon Nuclear Power Plant, Bechtel Power Corporation, Cogimet, Lindamood-Bell Leaning Processes, Fred Streit Associates, Division of Business and Economic Research of the University of Wyoming, County of San Luis Obispo, and the California State Water Resources Control Board. He helped establish the university-wide statistical consulting service at Cal Poly and has extensive consulting experience within the university. Dr. Smidt has contributed to journals such as Biometrics, Journal of Clinical Neuropsychology, Medical Anthropology, Journal of the Lepidopterists' Society, and American Journal of Orthodontics and Dentofacial Orthopedics, and to collections such as Statistical Case Studies, A Collaboration Between Academe and Industry and Familias y relaciones de genero en transformacion.

Excerpt. © Reprinted by permission. All rights reserved.
Preface Introduction

The primary questions that must be answered when a new statistics text for engineers and scientists is written relate to the issue of the contribution of the textbook to the pedagogy of teaching statistics to this audience of students and to how the text will differ from the many texts that are already available. These questions can be answered for the proposed text only in the context of recommendations that have been made as the result of a 1984 conference on the statistical education of engineers Hogg(1985) and a 1993 Quality Engineering Workshop Hogg(1994). Among the recommendations made was that engineers need to appreciate the following statistical concepts:

omnipresence of variability;

the use of simple graphical tools such as run charts, histograms, scatter plots, probability plots, residual plots, and control charts;

basic concepts of statistical inference;

the importance and essentials of carefully planned design of experiments;

the philosophies of Shewhart, Deming, and other practitioners concerning the quality of products and services.

The Hogg(1994) article proposed a core course of topics for engineering students. This proposed text is based on the curriculum model presented in that article.

Educational Philosophy

In our many years of teaching introductory statistics courses to students majoring in a wide variety of disciplines, we have continually searched for ways to improve the teaching of these courses. Over the years, our vision has come to include the following:

Students need a frame of reference when learning a subject, especially one that is not their major. This frame of reference for engineering and science students should be applications to the various areas of engineering and the sciences. The discussion of each statistical topic should include references to at least one of these areas of application.

Virtually all the students taking introductory statistics courses for engineers and scientists are majoring in areas other than statistics. Introductory courses should, therefore, focus on the underlying principles that are important for nonstatistics majors.

The use of spreadsheet and/or statistical software should be integrated into all aspects of the introductory statistics course. The reality that exists in the workplace is that spreadsheet software (and sometimes statistical software) is typically available on one's computer desktop. Our teaching approach needs to recognize this reality and make our courses more consistent with the workplace environment.

Textbooks that use software must provide detailed instructions that maximizes the student's ability to use the software with a minimum risk of failure.

Instruction for each topic should focus on (1) the application of the topic to an area of engineering or the sciences, (2) the interpretation of results, (3) the presentation of assumptions, (4) the evaluation of the assumptions, and (5) the discussion of what should be done if the assumptions are violated. These 'points are particularly important in regression, forecasting and in hypothesis testing. Although the illustration of some computations is inevitable, the focus on computations should be minimized.

Both classroom examples and homework exercises should relate to actual or realistic data as much as possible. Introductory courses should avoid an over-concentration on one topic area and instead provide breadth of coverage of a variety of statistical topics. This will help students avoid the "I can't see the forest for the trees" syndrome.

The main features of this proposed text are summarized in the following sections.

Main Feature: Emphasis on Data Analysis and Interpretation of Computer Output

The personal computer revolution has dramatically changed how information is analyzed in the workplace and how statistics should be taught in the classroom. In this text, we take the position that the use of computer software in the form of a spreadsheet application such as Microsoft Excel or a statistical package such as MINITAB is an integral part of learning statistics. We emphasize analyzing data, interpreting the output from Microsoft Excel and MINITAB, and explaining how to use this software while reducing the emphasis on computation. In order to carry out our approach, we have integrated this output into the fabric of the text. For example, our coverage of tables and charts in Chapter 2 focuses on the interpretation of various charts, not on their construction by hand. In Chapter 9 on hypothesis testing, we have made sure to include extensive computer output so that the p-value approach can be used. The presentation of simple linear regression in Chapter 12, assumes that software such as Microsoft Excel or MINITAB will be used, and thus our focus is on the interpretation of the output, not on hand calculations (which have been placed in a separate section of the chapter).

Main Feature: Problems, Case Studies, and Team Projects

"Learning" results from "doing." This text provides the student with the opportunity to select from many problems (most with multiple parts) presented at the ends of sections as well as at the ends of chapters. Most of these problems use real data and apply to realistic situations in various fields of engineering and the sciences. Students can aid their comprehension by engaging in multiple hands-on exercises as detailed below.

The end-of-section problems give the students the opportunity to reinforce what they have just learned.
The chapter review problems included at the end of each chapter are based on the concepts and methods learned throughout the chapter.
Answers to Selected Odd-Numbered Problems appear at the end of the text.
Detailed Case Studies are included at the end of several chapters.
The Whitney Gourmet Cat Food Company case study is included at the end of most chapters, as an integrating theme.

Main Feature: Appendices on Using Microsoft Excel and MINITAB

Rather than rely on the supplementary manuals, that accompany statistical software packages, it is a much better pedagogical approach to provide an explanation of how the software is used in the text while employing the in-chapter examples. Detailed appendices are included at the end of all chapters that explain how to use MINITAB, the most popular statistical software for introductory business statistics, and Microsoft Excel, the dominant spreadsheet package. In addition, an appendix is provided after Chapter 1 that explains the basics of the Windows operating environment.

Main Feature: Statistics Add-In for Microsoft Excel—PHStat

The CD-ROM that accompanies the text includes the PHStat Statistics add-in for Microsoft Excel that facilitates its use in introductory statistics courses. Although Microsoft Excel is a spreadsheet package, it contains features that enable it to perform statistical analysis for many of the topics in this text. In some cases, however, such analyses are cumbersome in the off-the-shelf version of Microsoft Excel. The PHStat statistics add-in provides a custom menu of choices that leads to dialog boxes which enable users to make entries and selections to perform specific analyses. PHStat minimizes the work associated with setting up statistical solutions in Microsoft Excel by automating the creation of spreadsheets and charts. PHStat, along with Microsoft Excel's Data Analysis tool, now allows users to perform statistical analyses on virtually all topics covered in this text.

Main Feature: Pedagogical Aids

Numerous features designed to create a more stimulating learning environment throughout the text include:

conversational writing style;
a Using Statistics example that illustrates the application of at least one of the statistical methods covered in each chapter in engineering and the sciences;
real data for many of the examples and problems;
exhibit boxes that highlight important concepts;
comment boxes that focus on assumptions of statistical methods;
problem sets with varied levels of difficulty and complexity;
key terms;
chapter opening quotes from a philosopher, historical figure, well-known statistician or from literature;
chapter ending problems that begin with Checking Your Understanding problems that require students to demonstrate their understanding of concepts;
explanation and illustration of statistical tables;
side notes in which additional material appears adjacent to where it is referenced.

Main Feature: Statistical Topics Covered

The text focuses on such topics as tables and charts (Chapter 2), descriptive statistics (Chapter 3), control charts (Chapters 6 and 7), experimental design (Chapters 10 and 11), regression (Chapters 12 and 13), and statistical inference (Chapters 8 and 9). This emphasis is consistent with the recommendations presented by Hogg(1994).

Perhaps the important statistical method used by engineers in industry is experimental design. Simply stated, engineers need to know how to conduct experiments where multiple factors are varied. Thus, in addition to coverage of one- and two-factor designs, this text discusses the concept of interaction in depth. Further, it provides coverage of factorial and fractional-factorial designs, using both a graphical approach and a confirmatory hypothesis-testing approach. In addition, the contributions of the Japanese engineer Genichi Taguchi are introduced.

By providing this comprehensive coverage of quality and experimental design, the text provides an orientation that allows the presentation of statistical tools in an organizational context, instead of in isolation. The goal is for students to learn not just how to use the tools but why and how statistical methods are useful in a wide variety of industrial settings. A portion of Chapter 1 is devoted to quality management, including both key themes and the contribution of individuals such as W Edwards Deming, Joseph Juran, and Walter Shewhart.

Main Feature: Full Supplement Package

The supplement package that accompanies this text includes:

Instructor's Solution Manual—written by M&N Toscano. This solutions manual is enriched with extra detail in the problem solutions and many Excel solutions. ISBN 0-13027423-2.
Student Solutions Manual—written by M&N Toscano. To order this supplement for your students, use ISBN 0-13-028681-8.

About the World Wide Web

The text has a home page on the World Wide Web with an address of http://www.prenhall.com/levine. There is a separate home page on the World Wide Web for the PHStat add-in, http://www.prenhall.com/phstat that provides user assistance and periodic updates.

Acknowledgements

We are extremely grateful to the many organizations and companies that allowed us to use their data in developing problems and examples throughout the text. We would like to thank American Cyanamid Company, American Society for Testing and Materials, Biometrika, Environmental Progress, Graphics Press, Journal of Energy Resources Technology, Journal of Engineering for Industry, Journal of Structural Engineering, Journal of the Minerals, Metals and Materials Society, Journal of Water Resources Planning and Management, New England Journal of Medicine, Newsday, Noise Control Engineering Journal, Philosophical Transactions of the Royal Society, Quality and Reliability Engineering International, Quality Engineering, Quality Progress, Technometrics, The American Statistician, and The Free Press.

We would also like to express our gratitude to David Cresap, University of Portland, Dr. C. H. Aikens, The University of Tennesee-Knoxville, and Dr. Robert L. Armacost, University of Central Florida for their constructive comments during the writing of this text.

We offer special thanks to Kathy Boothby Sestak, Joanne Wendelken, Ann Heath, and Gina Huck of the editing team at Prentice Hall, and to Bob Walters our Production Editor. Thanks also to Brian Baker for his copyediting and M&N Toscano for their accuracy checking.

David M. Levine
Patricia P. Ramsey
Robert K. Smidt

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