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Statistics for Engineering and the Sciences (4th Edition), by William Mendenhall, Terry L. Sincich
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This book illustrates basic statistical concepts with extensive applications in engineering and scientific contexts. The book includes optional theoretical exercises, allowing readers who choose to emphasize theory to do so with requiring additional materials. The fourth edition contains SAS and MINITAB computer printout results for all analyses performed—plus new exercises based on magazine and journal articles and news reports. KEY TOPICS: A section on "Detecting Normal Distributions" (Chapter 5) gives readers insights on when it is reasonable to assume that underlying data is normally distributed. There is a comprehensive example on model building (Chapter 13) and emphasis on the regression approach to a Nova (also presents the traditional approach). There are two sections discussing principles of experimental design, i.e., noise-reducing and volume-increasing design, a section on "Total Quality Management" and coverage of statistical computing. There are optional, calculus-based theoretical exercises, and real data sets, extracted from scientific studies, are provided in an appendix. Numerical answers to all applied exercises are included in an appendix—giving readers immediate feedback on their work.
- Sales Rank: #1820249 in Books
- Published on: 1995-01-13
- Original language: English
- Number of items: 1
- Dimensions: 9.50" h x 1.93" w x 8.30" l,
- Binding: Hardcover
- 1008 pages
From the Back Cover
This book illustrates basic statistical concepts with extensive applications in engineering and scientific contexts. The book includes optional theoretical exercises, allowing readers who choose to emphasize theory to do so with requiring additional materials. The fourth edition contains SAS and MINITAB computer printout results for all analyses performed—plus new exercises based on magazine and journal articles and news reports. A section on "Detecting Normal Distributions" (Chapter 5) gives readers insights on when it is reasonable to assume that underlying data is normally distributed. There is a comprehensive example on model building (Chapter 13) and emphasis on the regression approach to a Nova (also presents the traditional approach). There are two sections discussing principles of experimental design, i.e., noise-reducing and volume-increasing design, a section on "Total Quality Management" and coverage of statistical computing. There are optional, calculus-based theoretical exercises, and real data sets, extracted from scientific studies, are provided in an appendix. Numerical answers to all applied exercises are included in an appendix—giving readers immediate feedback on their work.
Most helpful customer reviews
7 of 8 people found the following review helpful.
Good for intuition
By A Customer
I think this book is very good for grasping the concepts and intuition behind the mathematical formalisms. Perhaps it should be used with another more theoretical book (Meyer, for instance). It is useful not only for engineering students.
4 of 4 people found the following review helpful.
Simple Statistics
By Maria Maliagros
If you are looking for a statistics book that explains things in a simple and elementary way, this book is for you! Additionally, the problems are straightforward. However, it requires that you know calculus I for some of the problems. It also has computer applications that you can practice. The only drawback is that the probability chapter is skimpy. You may want to supplement your knowledge of probability with another book, since statistics requires that you know probability first.
4 of 4 people found the following review helpful.
Difficult to read. Chaotic at best.
By Underdog
This book is very hard to read. The delineation between sections is difficult to determine. Referencing is almost impossible. The samples problems are confusing and often obtuse. Many explanations are unclear as presented.
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