Most Helpful Customer Reviews
10 of 12 people found the following review helpful:
5.0 out of 5 stars
My favorite design book, January 28, 2004
This review is from: A First Course in Design and Analysis of Experiments (Hardcover)
I have taught from this text at the senior/applied masters level three times, and my enthusiasm increases as time goes on. Students generally share my favorable opinion. I have also taught using other texts (incuding Montgomery), and this one is my current favorite. Oehlert takes design seriously, not just the analysis of experiments. He is obviously an experienced statistician with deep knowledge of both practice and theory. As a bonus, he writes well and uses excellent real examples. Aspects of the text that stand out as different from other texts include:
1. A detailed explanation of different error rates for multiple comparisons. More detail is present than is useful for a beginning student, but the exposition is excellent as a reference for those who need it.
2. An unusually well-informed (and practical) discussion of assumptions.
3. Discussion of SAS Type II errors. Common practice is not always the most sensible, and the author's advocacy of the Type II approach for many problems is compelling.
4. A good discussion of mixed model assumptions (restricted and unrestricted models). I have not seen a comparable exposition of this potentially confusing issue.
5. The use of Hasse diagrams for mixed models. I had not worked with Hasse diagrams before I used this text, but find them to be useful tools for analysis of complex designs. My only quibble is that some items in chapter 13 could be introduced earlier....although probably not fully covered. In particular, RCBDs without interactions could appear (with appropriate caveats) along with factorial designs. I confess some ambivalence on this issue, noting that I only quarrel because I am starting to get rushed for time by chapter 13. If you want a cookbook, go elsewhere. If you want a highly mathematical approach, this is also not for you. For a serious treatment of real statistical issues, however, both analysis and design, I doubt if you can do much better.
Help other customers find the most helpful reviews
Was this review helpful to you? Yes
No
10 of 14 people found the following review helpful:
2.0 out of 5 stars
Not very good, April 15, 2003
By A Customer
This review is from: A First Course in Design and Analysis of Experiments (Hardcover)
We are using this book for a Masters level graduate course in Experimental Design. The book is poorly written. It seems like it is basically a compilation of lecture notes. I would use Montgomery over this text. Even our professor admits that it is useless to study from. The plus points are that it covers certain things that other text books don't (Error Rates, SNK, etc.). These are only covered minimally, though, and don't make up for the poor coverage of most of the other subjects in the book.
Help other customers find the most helpful reviews
Was this review helpful to you? Yes
No
6 of 9 people found the following review helpful:
5.0 out of 5 stars
Above the field, September 11, 2005
This review is from: A First Course in Design and Analysis of Experiments (Hardcover)
I have taught a design course using Montgomery's text and found it tiring. A more appropriate title to that book would be "Experimental Design for Industrial Engineers," as the overwhelming majority of examples and exercises were of the I.E. persuasion. For a class of Statistics majors, the text was very difficult to motivate. Many students stopped using it and just relied on lecture notes. After the myriad of complaints, I spent Spring Break searching for a better book. A colleague recommended Oehlert's text. Chapter 2 on randomization methods was a perfect foray into the Normal-theoretic ANOVA, which is incredibly well-developed in Chapter 3. Theoretical arguments are juxtaposed with solid examples in what can best be described as an expository triumph. Chapter 6 on checking assumptions should technically come next (and I cover next it in my course), followed by contrasts in Chapter 4 and multiple comparisons in Chapter 5. The author does a very good job avoiding the "cookbook" approach to multiple comparisons so often seen in other texts. Chapter 7 on power analysis and sample size determination is one of the best I have read, even though most instructors omit this topic. A stylistic split comes thereafter: go to blocking designs or factorial treatment structures? I followed the author's lead, but one could go either way. Chapter 11 introduces random-effect models, with Chapter 12 building off of that with mixed-effects models and nesting. One of this book's strongest features is in Chapter 12: Hasse diagrams for deriving expected mean squares. Chapter 13 introduces variance reduction through blocking, though more detailed examples are needed. This is as far as I was able to get in one semester. I had planned on covering Chapter 14 on incomplete block designs, which upon my initial reading also seemed to lack detailed examples. In terms of software emphasis, the author clearly prefers a package from his university. It is freely downloadable, but requires the learning of new syntax. For my class, I offered both R and SAS demonstrations as a supplement to the text, with researchers preferring the former and practitioners preferring the latter. Some students relied on SPSS. After Spring Break, I referred my class to this book. My summer course then used this book as the required text, and it seemed to be very well-received. As far as I know, it is still being used now.
Help other customers find the most helpful reviews
Was this review helpful to you? Yes
No
|
