A Modern Introduction to Probability and Statistics: Understanding Why and How (Springer Texts in Statistics) by F.M. Dekking

by F.M. Dekking

Probability and Statistics are studied by most science students, usually as a second- or third-year course. Many current texts in the area are just cookbooks and, as a result, students do not know why they perform the methods they are taught, or why the methods work. The strength of this book is that it readdresses these shortcomings; by using examples, often from real-life and using real data, the authors can show how the fundamentals of probabilistic and statistical theories arise intuitively. It provides a tried and tested, self-contained course, that can also be used for self-study.A Modern Introduction to Probability and Statistics has numerous quick exercises to give direct feedback to the students. In addition the book contains over 350 exercises, half of which have answers, of which half have full solutions. A website at www.springeronline.com/1-85233-896-2 gives access to the data files used in the text, and, for instructors, the remaining solutions. The only pre-requisite for the book is a first course in calculus; the text covers standard statistics and probability material, and develops beyond traditional parametric models to the Poisson process, and on to useful modern methods such as the bootstrap.This will be a key text for undergraduates in Computer Science, Physics, Mathematics, Chemistry, Biology and Business Studies who are studying a mathematical statistics course, and also for more intensive engineering statistics courses for undergraduates in all engineering subjects.

Genres: Mathematics, Nonfiction, Science, School, Computer Science, Textbooks, Reference

Hardcover

First published February 14, 2006

Reviews

[Navid Ghorbanali · Rating 5 out of 5]

One of the best introductory statistics books ever written. The examples are well motivated and interesting, and the writers do a phenomenal job on building up the intuition behind the methods and the math - the one that always sticks out to me is the 'computer chip' example when introducing the key idea behind Maximum Likelihood Estimation.

A joy to read and a superb choice for an undergraduate level introduction, an overlooked gem.

[Rebecca · Rating 3 out of 5]

I could not read it before/during my course since the text was too hard, but I hope I can read it later to refresh my memory after the course. The formulas are presented well in grey boxes, the figures, exercies and solutions are good.

[Sourav Chakraborty · Rating 4 out of 5]

A decent book for the introduction of the subject. A concise and example driven text with exercises.

[Prim A. · Rating 3 out of 5]

surely this counts as 'reading' right

[Darin · Rating 5 out of 5]

This has become one of my favorite statistics book. It consists of 28 chapters on specific topics (e.g., Central Limit Theorem) and includes topics that are unusual in most statistics book. Some of the material is calculus-based, making this text somewhat like a mathematical statistics book. I wouldn't recommend this as someone's introduction to statistics although it might be OK as a supplement to another book. The chapters are short and focus on the big picture.

[Ed van der Winden · Rating 5 out of 5]

I worked my way through this book, doing many of the exercises, to get (re)aquainted with the subject. When you put in the work it goes a long way in explaining quite a large amount of both probability and statistics.

[Melissa · Rating 4 out of 5]

Good book, but hard to follow sometimes.