Here is a listing of books which might or might not be helpful to you in this course.  I've included my own brief comments, but would welcome to hear yours.  If you are familiar with the book, and a student in this class (UCLA X401), please send me email (rgould@stat.ucla.edu) and let me know your thoughts.  Also, please include a brief description of how well you know the book. For example, have you merely skimmed it, or used it as a textbook yourself, or read it carefully from cover to cover?

1) Chatfield, Christopher, Problem Solving: A Statistician's Guide, Chapman and Hall.

I think this is a great book, although perhaps it is not for everyone and certainly not for all occasions. This book does not contain any techniques or "how to"s.  The first half of the book offers a philosophy of statistics and, more importantly, guiding principles for approaching statistical problems. It is also filled with helpful practical details, including what to look for in software, useful books and resources, and how to write a report.  In my opinion, some of the chapters in this first half of the book are invaluable.  The second half of the book consists of data sets and exercises, with discussion. Some of these data sets are somewhat artificial, and some of the solutions are idiosyncratic and  some use quite advanced techniques.  The explanations are often rather curt, and assume the reader is  familiar withthe techniques but maybe inexperienced in their application. [RG]

2) Cox, D.R. and Snell, E.J. Applied Statistics: Principles and Examples.

Chatfield's book owes a debt to this one both in its structure and its general philosophy.  However, in my mind, Chatfield comes out on top.  Cox and Snell's book also has two sections.  The first is a "how to" and explains many principles and concerns of applied statistics.  The second half gives data and examples of analyses.  Both books have interesting first halves, but Chatfield's comes across as more prescriptive and Cox and Snell's more descriptive, at least to my way of thinking.  I prefer Chatfield's approach, but this is a matter of personal taste.  Also, I think that to really get much out of Cox and Snell, you need to already have a fair amount of experience.  Certainly, to understand their examples requires  considerable sophistication sometimes.  Still, I think the data sets are valuable if they are used as practice problems, and while the explanations might be intimidating, they are occasionally illuminating. [RG]

Note that neither of these two books  (Chatfield or Cox & Snell) are good references if you want to learn or brush up on particular techniques.  But both are good ways of learning to see how (at least some) statisticians think and solve problems.

(3) Box, George E.P., Hunter, William G., and Hunter J. Stuart, Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. Wiley & Sons.

This book focuses on designed and controlled experiments.  Most of the examples and data sets are realistic, but not real.  The applications are oriented towards engineering and industry, and sometimes, for example the discussion on the scientific process, the explanations are fairly idealized.  Also, this book assumes familiarity with probability and statistical theory.  With that said, it gives formulas for standard tests and techniques, and offers advice that sounds based on considerable experience.  (For example, when one should and shouldn't use the t-test.)  Also, it has a nice explanation of when to block and not to block while designing experiments. [RG]

(4) Ramsey and Schafer, The Statistical Sleuth: A course in methods of data analysis.  Duxbury.

I'm not that fond of the overall layout of the book (which to my way of thinking encourages an approach that looks for the right data to be applied to the techniques, rather than showing you the techniques you can apply to solve your problems), and the explanations are not always that clear.  But this is written at a more accessible level than the Box, Hunter & Hunter book, and has a wider variety of examples.  The book has many nice case studies which provide a good context for the techniques discussed, and it has formulas for those wishing to look up certain techniques.  In short, I think it might be a good reference book, but I wouldn't rely on it to learn a topic.  The worst fault is a tendency to quickly sweep perplexing problems under the rug.  For example, in the section on logistic regression: "The natural choice of link for a normally distributed response...is the identity link."  This begs the question, of course, of why this is the natural choice.  Another example in Chapter 3:  they discuss applying the t-test to compare two groups.  They admit the data are not symmetrically distributed, but say that "it doesn't matter" with no explanation of why it does not. However, Allen Martin told me he feels that some of these "brush offs" are resolved more completely in later chapters.  So perhaps the book is best if read cover to cover, but not as good for opening up a chapter to learn about a particular topic.  [RG]