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This is the most widely used mathematical statistics text at the top 200 universities in the United States. Premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid undergraduate foundation in statistical theory while conveying the relevance and importance of the theory in solving practical problems in the real world. The authors' use of practical applications and excellent exercises helps students discover the nature of statistics and understand its essential role in scientific research.
- Sales Rank: #446483 in Books
- Brand: Brand: Duxbury Press
- Published on: 2001-05-30
- Original language: English
- Number of items: 1
- Dimensions: 1.48" h x 7.68" w x 9.52" l,
- Binding: Hardcover
- 880 pages
- Used Book in Good Condition
About the Author
Richard L. Scheaffer, Professor Emeritus of Statistics, University of Florida, received his Ph.D. in statistics from Florida State University. Accompanying a career of teaching, research and administration, Dr. Scheaffer has led efforts on the improvement of statistics education throughout the school and college curriculum. Co-author of five textbooks, he was one of the developers of the Quantitative Literacy Project that formed the basis of the data analysis strand in the curriculum standards of the National Council of Teachers of Mathematics. He also led the task force that developed the AP Statistics Program, for which he served as Chief Faculty Consultant. Dr. Scheaffer is a Fellow and past president of the American Statistical Association, a past chair of the Conference Board of the Mathematical Sciences, and an advisor on numerous statistics education projects.
Most helpful customer reviews
33 of 34 people found the following review helpful.
Anything but Wackerly
By Danial Lee Sellers
This is an adequate text up until chapter 6. The probability chapter the the discrete probability distributions are great. The concepts and derivations are logically laid out and make sense. Things begin to get frustrating in chapter 4 and really fall apart after chapter 6. After chapter four the author no longer tries to put anything in context. He derives very little and simply states long, cryptic formulas and expects the reader to magicaly understand the big picture and fill in the gaps on their own. He skips over the over 200 years of the development in statictical theory and simply states the results and the student is expected to fill in the gap. The most self evident properties are expounded on in the chapters and the most important and subtle properties are hidden in the exercises or neglected altogether. I am nearly finished with my undergraduate degree in applied mathematics and am mathematicaly mature. I am comfortable with both applied and pure/proof-type mathematics and this text makes my stomach turn. It makes me want to reconsider my major. I am giving this text 2 stars simply because the first 5 chapters are adequate. If you just need a course in probability, this might be the text for you. If you need to move on to statistical inference and you need the big picture/contextual background explained rather than pluging and chuging blindly with nonsensical formulas, then this is not the text for you. Way to drop the ball, Wackerly!
7 of 10 people found the following review helpful.
Solid Presentation But Lacking Concision
By Andrew Adams
The sixth edition of Math. Statistics w/ Applications is a solid book with good information. However, the form of presentation was not for me. The authors have chosen an explicative style which tends to be overly wordy.
When I begin to read a chapter, I tend to get frustrated and impatient because they either try to show you all the subtleties at once or give you a long-ass paragraph that can be said with one sentence. Thus, The most important stuff is buried in a mountain of over-whelming text.
I have ditched this book in favor of Ghahramani's "Fundamentals of Probability, Second Edition" for my Math Stat I class.
22 of 27 people found the following review helpful.
Good reference book overall
By FizzWiz
Overall, a good book. Could be a better even after excluding minor details- the ones noticed mentioned below.
Background necessary: Calculus- especially Taylor series, Integration by parts, solving for areas by integration and figuring out their limits; Even with a sufficient background, some proofs are quite rigorous or not well written enough, not sure which sometimes.
Comments based on first 7 Chapters of the book:
Standard deviation- excellently described
Relative frequency- NOT very well described. Makes sense on looking at chart in the book, but readers may forget to look at chart. A good definition for this important term is needed.
p. 43 They mention multinomial expansion before binomial distribution 1 chapter later. Very unnecessary, and doesn't make sense for the general reader to just stick it in there. It's like trying to squeeze information just to try to put something mathematically interesting that is more of a hinderance for learning permutations, combinations, and the multiplication rule. They show it to try to combine the principles together, but to make any sense of the multinomial expansion, besides choosing numbers to do it out, the proof to understand it fully is graduate level mathematics. A big no no for the book!! The least the book could do is expand by showing a specific example of this, and maybe even the proof itself, or just not mention it at all.
Section 2.7, p. 50 intro- too wordy. "relative frequencey of occurrence" in parentheses should probably be taken out for less confusion since a rel. freq. does not have to be based on a conditional probability occurring. Leaving it in leaves the reader at possible ambiguity.
Section 2.9, p. 62 Unnecessarily complicated- 1/5 * 3/4 = answer makes a lot more sense than them breaking it down into an unnecessary 7+ steps = answer.
Binomial Theorem probably could've been presented better. The idea of the binomial expansion and definition 3.7 (binomial distribution theorem) should be explained together right after definition 3.6 (explanation of a binomial experiment) rather than confusing the reader and leaving it off until later on.
p. 102-104 The book does a good job relating some word problems to the real world in good, brief explanations.
p.152 Step function should show hollow points if not the filled end points as well at least- too sloppy.
p. 172 Example 4.9 well done, but makes it sound like = (y- u)/o was intuitively obvious even to the beginner. The book tends to introduce some examples like this to introduce a theory. Just something one needs to get used to. It would be nice if they were able to put a sentence on page 1 about how some conecepts are inrtoduced by example first, but of course not everyone would read it anyway.
p. 188, sec. 4.8- great summary explaining existence for variety of statistical models
Example 5.1, p. 212- not a very good example. The table's correlation to the sample space given is overly ambiguous. Example is confusing, especially since they use the starting point starting from the top right corner instead of the top left corner and show no indication of what stands for what without reader figuring it out. Poorly set up.
p.213 typo- 6/9, not 8/9- tricky to figure out for a beginner
p.225 calculation- just add "= 1" to show solving of one of the integrals. Book sloppily leaves out this simple, but crucial step. A hard read for beginners to notice.
"degrees of freedom (d.f.)" can probably be better and simply defined than the book's "attempts," such as always one less than number of trials or something like that rather than always showing d.f. by notation only.
It's quite arguable if maybe just normal, or normal and t distributions and then central limit theorem should be mentioned in the book first, rather than throwing in all the different kinds of distributions in to learn for Chapter 3 first.
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