This book provides the most comprehensive and in-depth treatment of the univariate and bivariate Normal distributions (for multivariate Normal see a book by Y. L. Tong). Both probability and statistics applications are considered. If you need an even deeper treatment, virtually every formula cites a source. This is a great reference book.

This one rates "highly recommended" and I specify it as a suggested reference for my undergraduate mathematics refresher course.

This book is an AMAZING introduction to the Theory of Numbers. It assumes no previous exposure to the subject, or any technical mathematical knowledge for that matter. Its prose is lucid and the style appealing. Davenport chose NOT to write a lemma-theorem-proof kind of book, and the result is a marvelous, eminently readable introduction to the subject. Its wonderful to read a book where good prose is used to appropiately substitute a massive collection of uninviting symbols. I've also been reading other books on Number Theory, such as Hardy & Wright, but none are as clear as this one.

I found the chapter on quadratic residues (which includes the reciprocity law) to be especially well written. The section on computers and number theory is excelent as well. A concise and coherent discussion of crytography and the RSA system is included here. The organization of the book's chapters is fantastic. Each chapter builds up on results proven in the previous ones, showing well the connections between the different aspects of Number Theory. The exercises of the book range from simple to challenging, but are all accesible to someone willing to put effort into them.

This would be an excelent source for learning number theory for mathematical competition purposes, such as the ASHME, AIME, USAMO, and even for the International Mathematical Olympiad. The book contains much more than what is needed for these competitions, but the olympiad/contest reader will benefit greatly from a study of Davenport's work.

The book can certainly be used for an undergraduate course in Number Theory, though it might need supplementary materials, to cover a semester's worth of work. I know the book has been used in the past in previous editions as the main text for Math 124: Number Theory at Harvard University.

I would also recommend this book to anyone interested in acquanting themselves with Number Theory.

Awesome! There is simply no other word that describes The Higher Arithmetic.

Confused by the basic concept of a limit? Just memorizing stuff to get through calculus, and wondering what it is really all about? This book, referred to as an "old pot-boiler" by one of its authors (Torrance)was written back before calculus was a required course. It actually explains the basic concepts, and takes time to build the theory from the ground up. Burrington and Torrance did such a careful job with the concepts, without skipping steps, or substituting slick shortcuts, that it was used for a couple of decades as an alternate text after newer texts were written. After all, any calculus text that stayed in print for over 25 years can't be all bad !! The book is also of interest to anyone concerned with the history of scientific ideas, or the history of the teaching of higher math.
Disclaimer: reviewer is the daughter of one of the authors.

Hald is a well known Danish statistician who previously published a book titled "A History of Probability and Statistics and Their Application before 1750". This book is a sequel to that one. I have not read his earlier work but have read other fine historical accounts by Stigler, Porter and most recently "The Lady Tasting Tea" by Salsburg. This book is up to the high standards set by these other fine authors.

Prior to the late 1800s there was very little theory for statistics. There were many interesting developments in probability prior to 1750 and nearly all of them dealt with gambling situations. One does not need to read Hald's earlier work to be up on these writings as he summarizes many of the key works of James and Nicholas Bernoulli, and de Moivre in Chapter 2 along with the post 1750 work of Laplace and Lagrange.

His Preface describes the aim of the book and relates it to other works. Chapter 1 then maps out the plan of the book. The first three parts of the book cover the period from 1750 to 1853 and the final part covers selected developments in estimation theory from 1830-1935. Part 1 deals with direct or frequentist probability as it developed from 1750 to 1805. Part 2 deals with inverse probability or subjective (Bayesian) probability as it developed from the posthumous publication of Bayes' treatise by Price in 1764 (Bayes died in 1761) and developed as a principle of probability by Laplace in 1774 to its continued development through 1812. Laplace's principle of indifference was rekindled with further developments in Bayesian methods by Jeffreys in the 1930s. Part 3 begins with Gauss in 1809 and covers the early history of the central limit theorem, least squares and the normal distribution. This covers mainly the period from 1810 to 1853 but later related work is also mentioned. Finally Part 4 deals with important select topics in estimation theory from 1830 - 1935.

Hald is thorough and scholarly in the tradition set by Steve Stigler. This is a massive work of 739 pages with an additional 35 pages of bibliography.

Prominent figures in Part 1 include Laplace, de Moivre, Lagrange, Boscovich and Daniel Bernoulli. Part 2 covers the work of Bayes, Price, James Bernoulli and primarily Laplace. Part 3 deals with Laplace, Poisson, Bessel, Cauchy and Gauss. In Part 4 we meet Bienayme, Cauchy, Gram and Schmidt and their orthogonalization process, Quetelet, Condorcet, Cournot, Galton, Thiele, Karl Pearson, R. A. Fisher, Gosset and Edgeworth.

Fittingly the final chapter, Chapter 28 covers the theory of mathematical statistics as it was developed by Fisher from 1912-1935.

This is a great reference source for anyone who wants to collect and cherish the major developments of probability and statistics.

There is still room for a third book covering the period from 1930 to 2000 when the Neyman and Pearson theory of hypothesis testing developed, Bayesian statistics was revitalized, statistical decision theory and sequential analysis developed as did multivariate analysis, time series analysis, robust statistics, quality control methods, spatial statistics and resampling methods. The late 20th and early 21st centuries have seen many advances based on the ability to do intense calculation on amazingly fast computers!

Induction, Probability and Causation : Selected Papers is a masterpiece of analytic thought. In this text Charles Dumbar Broad open up's right in our face all the problems that the question about induction brings to the comprension of the phisical theories about the reality. C. D. Broad, practices a rigoruos review on the conceptions that the philosophers have mantained, along the philosophy history, to explain the nocions of probability and induction, since Autrecourt to Reichenbach passing through Hume, Keynes and Von Misses. Therefore Induction, Probability and Causation : Selected Papers of C.D. Broad, is a obligated palce to all who's interests take the ride over induction, causality and probability.

The authors are among the greatest mathematicians of this century. The contents of this book form the basis for countless applications of mathematics in economics, finance, engineering and physical sciences.

This is an excellent reference book for applied researchers who use advanced structural equation modeling (SEM) in social, psychological, and biomedical studies. The authors are well-known experienced experts in SEM, and the contents of this book are practical. Researcher who use different software in SEM can find their practical examples and related theoretical bases.

Siegrist has written a neat book which comes with a floppy disk. The book describes the probability concepts of the disk in detail, but the disk is the best part. A great tool for teaching basic probability and sampling concepts. Its only deficiency is that there ought to be a sequel with more simulations! Stephen Farish University of Melbourne, Australia

This is a great book that was a real help in getting me through school and in my first jobs afterwards. It provides the clearest explanation of various spatial statistical techniques that I found (after an extensive literature review for a Masters thesis). The software package is great, particularly because it allows the reader to see all the theory in action in a graphical way.