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This well-respected text is designed for the first course in probability and statistics taken by students majoring in Engineering and the Computing Sciences. The prerequisite is one year of calculus. The text offers a balanced presentation of applications and theory. The authors take care to develop the theoretical foundations for the statistical methods presented at a level that is accessible to students with only a calculus background. They explore the practical implications of the formal results to problem-solving so students gain an understanding of the logic behind the techniques as well as practice in using them. The examples, exercises, and applications were chosen specifically for students in engineering and computer science and include opportunities for real data analysis.
- Sales Rank: #262606 in Books
- Brand: Milton, J. Susan/ Arnold, Jesse C.
- Published on: 2002-09-30
- Original language: English
- Number of items: 1
- Dimensions: 9.50" h x 1.40" w x 6.60" l, 2.63 pounds
- Binding: Hardcover
- 816 pages
About the Author
J.Susan Milton is professor Emeritus of Satatics at Radford University. Dr. Milton recieved the B.S. degree from Western Carolina University, the M.A. degree from the University of North Carolina at Chapel Hill, and the Ph.D degree in Statistics from Virginia Polytechnic Institute and State university. She is a Danforth Associate and is a recipient of the Radford University Foundation Award for Excellence in Teaching. Dr. Milton is the author of Statistical Methods in the Biological and Health Sciences as well as Introduction to statistics, Probability with the Essential Analysis, and a first Course in the Theory of Linear Statistical Models.
Jesse C. arnold is a Professor of Statistics at Virginia Polytechnic Insitute and state University. Dr. arnold received the B.S. Degree from Southeastern state University, and the M.A and Ph. D degrees in statistics from Florida state university. He served as head of Statistics department for ten years, is a fellow of the American Statistical Association, and elected member of the International Statistics Institute. Ha has served as President of the International Biometric Society (Eastern North American Region) and Chairman of the statistical Educational Section of the American Statistical Association.
Most helpful customer reviews
15 of 16 people found the following review helpful.
a standard statistics book for engineers
By Stanislav Kolenikov
I taught a class at upper undergraduate level (mostly junior students) for a non-majors (mostly engineering students, some economics and political science) at a research university using this book.
It was written in the mid 1980s, and has not seen major updates even though it now comes in 4th edition. One of the assignments I gave was to comment whether you recognize the brand names of the mainframe computers mentioned in the problem. Newer stuff like say the bootstrap or machine learning or anything like that is not mentioned anywhere nearly. True, it has all the major results and techniques, but it was written by statisticians as the first course in statistics for to-be-statisticians, rather than the-only-course-in-statistics-you'll-ever-see for engineers. Thus a lot of things could have been presented in a different way with a different depth of exposure. Say the reliability, arguably a more important topic for engineers than moment generating function techniques, deserves a whole separate chapter, rather than being stuck in a middle of the chapter on continuous distributions. Simulations could have been highlighted throughout the book -- a good fraction of my students would probably be geekier than me with computers. I would unite all the confidence intervals under the umbrella of a single chapter, rather than presenting the CI for mean in one chapter and CI for variance in the next one. And so on. There even were errors in the answers in the end of the book, although you would probably expect the fourth edition not to have any.
Students complained a lot about the book in my class, too. Some said it did not help much, although there were others who did not come much to class (admittedly, I am a pretty boring lecturer) and got B's and A's, so apparently it was of some use to them. The price is of course also an issue: I personally won't pay $120 for book of this quality to sit in my professional library, and it sucks that I have my students buy it.
[Wasserman's [ASIN:0387402721 All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)]] is a much more modern book, although in all likelihood it would be difficult for my clientelle. My other favorite is Utts' Seeing Through Statistics (with CD-ROM and InfoTrac ), but this one is on the other side of technicality, being too easy. Finally, for engineering students specifically, Ryan's Modern Engineering Statistics (Hardcover) appears to be a much better text, although I have not taught from it, and my recommendation is based on just browsing through the pages and supplementing the current book with examples and problems from Ryan's book.
13 of 16 people found the following review helpful.
More garbage by professional academics
By thatstheticket
This book is used in introductory probability and statistics courses, yet it reads like the authors' awkwardly written cliff notes.
The authors insist on using multiple levels of mathematical indirection to introduce even the most basic of theorems. Examples in the reading consist mostly of long drawn out, and totally unreferenceable word problems. There are no diagrams to help visualize concepts until chapter four. Summaries are wordy. Summaries of similar topics are inconsistently worded leading to a lack of parallelism, and gratuitously increasing the difficulty of comparing them.
Overall, this is a useless book to learn or study from.
4 of 4 people found the following review helpful.
Wordy, bland, and needlessly complex
By Aerospace Engineer
This is the third course in probability I have taken over the last 30 years and have not found a good textbook yet. The book is pure drudgery to read. Overly wordy, complex explanations are the norm. Or as the authors probably would have put it: The text makes use of a multiplicity of non value added ink-on-paper replications of verbal communication to excessively inhibit the coherent transmission of information vis-a-vis the learning construct. Presentation of the math concepts is inconsistent, partly set aside in call outs and partly buried beneath tons of words in the text, and partly assumed from the ether. Problem is, you don't know which.
I really would like to know why statisticians feel the need to obscure what is really fairly simple math within a world of illogical terminology and backwards thinking. That is, the actual math is fairly simple assuming you can decipher the word problem and figure out which of the hundreds of formula to use.
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