Despite the restriction to discrete probability this book is a superb general introduction for the math undergraduate and is very well organized. Great text!!
Inventory on Biblio is continually updated, but because much of our booksellers' inventory is uncommon or even one-of-a-kind, stock-outs do happen from time to time. Near Fine. Finding a perfect analogy in the situation of the geometrical characters in Flatland, Professor Rucker continues the adventures of the two-dimensional world visited by a three-dimensional being to explain our three-dimensional world in terms of the fourth dimension. NonEuclidean Geometry. LL is great in having not too much interpretation though.
Gordon, Hugh. Discrete Mathematics. As a rule I think that the best books to learn probability from are those on modeling. For example, perhaps the best writer on probability is Sheldon Ross.
Introduction to Probability Models , 6 th ed. Karlin, Samuel. An Introduction to Stochastic Modeling , rev.
It is indeed a wonderful book: Hamming, R. The Art of Probability for Scientists and Engineers. Elementary Probability, 2 nd ed. Problems and Snapshots from the World of Probability.
The first volume is inspiring. The larger second volume is even more technical than the first, for example there is a chapter review of measure theory. Feller, William. Introduction to Probability Theory. Vol 2, 2 nd ed. The following is an inexpensive little reference. It requires only a basic knowledge of probability, say through Bayes' Theorem.
The great thing about it is that the problems are actually interesting. I have found this to be a good source for classroom examples. Mosteller, Frederick.
Fifty Challenging Problems in Probability with Solutions. Duelling Idiots and Other Probability Puzzlers. Fuzzy Stuff logic and set theory. Some books in this area are better than others. By in large though, it is a lot of bull about ad hoc, not particularly robust, algorithms.
korovskiy.com.ua/components/2019-10-24/1151-kozerog-muzhchina-goroskop.php Claims of anything new and profound are general pompous bullstuff. Fuzzy methods are trivial if you have knowledge of probability and logic. In my view the aspiring applied mathematician can not do better than to study probability. A book of practical statistics as opposed to mathematical or theoretical statistics is the one by Snedecor and Cochran. It is rigorous but does not use calculus.
It uses real life biological data for examples but is fascinating. It is a wonderfully well written and clear book. A real masterpiece. Anyone who actually does statistics should have this book. But remember, though it does not require calculus it does require mathematical maturity.
My feeling is that if you want to use this book but do not know calculus you should go back and take calculus. Snedecor, George W. Statistical Methods , 8 th ed. Iowa State. A great book. The best books about statistics for the layman are very likely: Tanur, Judith M. Statistics: A Guide to the Unknown , 3 rd ed. This is a great book. See also Bennett. Salsburg, David.
The Lady Tasting Tea. Without using a single formula it does a much better job of telling the layman what statistics is about than does the usual introductory text. It is also of interest to the professional. A classic applied book that is readable and thorough and good to own is: Neter, John, Michael K.
Kutner, Christopher J. Nachtsheim, William Wasserman. Applied Linear Statistical Models, 4th ed.
My favorite text on mathematical statistics is definitely the following. It is a large text with enough material for a senior level sequence in mathematical statistics, or a more advanced graduate sequence in mathematical statistics.
It is very well done. Dudewicz, Edward J. Modern Mathematical Statistics. Introduction to the Theory of Statistics. The Cartoon Guide to Statistics. An elementary book that does a nice job on statistical tests and which might be of interest to the practitioner is: Langley, Russell. Practical Statistics Simply Explained.
The book by Box, Hunter and Hunter is wonderful at exploring the concepts and underlying theory. The book by Saville and Wood is worth considering by the serious student.
Although its mathematics is simple and not calculus based this is the way theory was developed and this is also touched upon in the book by Box, Hunter, and Hunter. Hicks, Charles R. Fundamental Concepts in the Design of Experiments. Stuart Hunter, and William Gordon Hunter. Saville, David J. And Graham R. Statistical Methods: A Geometric Primer. My favorite book on regression is the one by Draper and Smith.
The book by Ryan is particularly elementary and thorough. Draper, Norman R. Applied Regression Analysis. Modern Regression Methods.