As a grad student faced with a statistics paper interpreting different probability models, this small book was the solution!
He is so clear and so thorough, I know how to interpret those results now! He clearly specifies that there are 5 ways to interpret these models and I have not found any other book/sage series that is more clear than this. I also have Aldrich and Nelson's "Linear Probability, Logit, and Probit Models" but this one's so much better than that if you are looking into how to interpret those coefficients. A MUST BUY! I was so surprised that no one wrote a review of this excellent piece, I just had to write it!

In one skinny unintimidating book that has less heft than an issue of TV Guide (that's physical heft, not literary worth), you get all the basics. Great place to start. Terms and equations are explained with simplicity. After reading just this book you'd be able to go do some time series analysis on your computer and have a clue what the output means.

Gerald Tenenbaum's lucid writing style provides a comprehensive, yet non-painful introduction to the subject. This book focuses on methods, which once learned are of utility both to the expert and novice. The beautiful---and sometimes obscure results---chosen as exercises enhance the appeal of the book. A must for number theorists and a text for anyone wanting to explore applications of analysis in an arithmetic setting.

This is about the 1st edition (1994 I think). This book is very well written and very practical in nature. If you need to apply signal analysis (time or frequency domain), without getting bogged down in theory, this will be a very good guide. It is a great introduction to the subject - from which you can then get into the more theoretical texts.

Introduction to Business Statistics by Kvanli is a really useful, clear, didactic classic directed to statistical application to many fields - including research in improbable areas as mine, uniting architecture and psychology. I strongly recommend it to the young researcher as an all encompassing introduction to statistics thinking.

Finally a statistics book that isn't just a math book with no practical implication.

This book really helped me at my job to make decision calculations.

This is a great introduction to the area for those already possessing good mathematical ability and knowledge of distribution theory. All the seminal theorems and references are in here and the reader would be wise to check them out. Well written and communicated, didn't find any typo's. Enjoyed it. This area is fast growing in the area of mathematical finance.

This book explains the methods of distance sampling, which is a statistical procedure for estimating the abundance and density of biological populations. This book outlines the statistical procedures, assumptions, and philosophy of distance sampling, and gives good advice for survey protocol and design.

This is an easy to read, gentle introduction to factor analysis. If you have struggled to find a readable resource on factor analysis then stop your search! I finished this book in an afternoon. I finally understand the basics of factor analysis. It's actually quite simple! You don't need more than an elementary understanding of expectation, variance, covariance and correlation.

The following adjectives describe this paper:

Well-written, Clear, Concise, Easy to understand, Non-mathematical (Basic statistics), Complete, Excellent diagrams

Do not hesitate to get this book!

I give it my highest recommendation!

Everyone with any interest in probability or combinatorics should take a look at this book, and at least read Chapter 1, on the Buffon needle problem. It gives a beautiful conceptual solution, quite different from the more well-known solution using integrals to get conditional probabilities. I find it hard to imagine anyone reading Chapter 1 and not deciding to read the entire book.

I heard Rota lecture on this material, and the book has much the same feeling as his lectures: it is clear, elegant, and concise, full of illuminating examples. Relatively little background is required, and it should be easily accessible to beginning graduate students (or undergraduates with unusually strong backgrounds).