Customer Reviews

A First Course in Design and Analysis of Experiments

5 star:		(4)
4 star:		(1)
3 star:		(0)
2 star:		(1)
1 star:		(3)

 

 

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.

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.

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.

The book is well written and contains a number of examples pulled directly from the real world. The book shows statistical output from several different statistics programs to help the reader get a better idea of what he or she would see in the real world.

So you may not notice it says under the book title: usually ships between 1-3 weeks! I still haven't received my copy (almost a month). So if your in a hurry like most graduate students: don't buy the book here.

Where can I find the solutions for the exercises in the book? It can really help me better understand the course!

Thanks!

Unlike the market-leaders that are showing their age, this book takes a modern point of view of experimental design, not excessively tilted towards industrial settings. It's also the only book showing how to use Hasse diagrams to find expected mean squares, which is by far the easiest way. The book also pays considerable attention to the design, not just the analysis, of experiments. It emphasizes practical, rather than mathematical, depth and insight. It's terrific.

Period. The author may be good in this field or research, but he definitely needs to improve his english language-Is he a native english speaker? Long long sentence, awkward structure, poorly chose words only leave reader in confusion.

This book is terrible, so much so that they stopped producing it in the year 2000. It has examples, but is missing any reasonable explanations for why he is doing things, then he skips straight to results and says for you to find your own methods to this problem. It also gives a series of equations that provide few if any examples using data, those that do use the data seem to skip so many steps it is not useful at all. Stick with Montgomery if you want a good DOE book.