Product Description

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.

Product Details

• Amazon Sales Rank: #180521 in Books
• Published on: 2007-02-14
• Released on: 2007-02-01
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 488 pages

Review

From the reviews:

"[the material is] superbly motivated with interest-grabbing examples... exercises excellent and plentiful." Edward Williams, University of Michigan-Dearborn, USA

"... it is a notoriously hard task to introduce probability and statistics with a mix of intuition and mathematics to keep students motivated. Therefore, I very much welcome this book and recommend it as course material." Sara van de Geer, Leiden University, The Netherlands 

"This textbook provides a well-written first course in probability and statistics...It is a book that has been written based on the long teaching experience of the authors and I would certainly recommend it for university coursework." Short Book Reviews of the International Statistical Institute,  December 2005

"This book has numerous quick exercises to give direct feedback to the students. … A website at www.springeronline.com/978-1-85233-896-1 gives access to the data files used in the text … . 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." (Rainer Beedgen, Zentralblatt MATH, Vol. 1079, 2006)

"The book is designed for a one-semester introductory course in probability and statistics basics for engineering students. … It can also be used by students in other more mathematically oriented majors such as applied mathematics with more emphasis on the mathematics and additional coverage in topics such as combinatorics, conditional expectation, and generating functions. … More elaborate exercises and real datasets are given at the end of each chapter." (Arthur B. Yeh, Technometrics, Vol. 49 (3), August, 2007)

About the Author

Michel Dekking, Cor Kraaikamp, Rik Lopuhaä and Ludolf Meester are professors in the Department of Applied Mathematics at TU Delft, The Netherlands. The material in this book has been successfully taught there for several years, and at the University of Leiden, The Netherlands, and Wesleyan University, USA, since 2003.

Customer Reviews

Excellent book, but needs proofreading
I have a strong general background in math, but not in probability and statistics. I use this book for self-study, and I find that it fits that purpose excellently. There are plenty of examples, and problems are adjusted so that they focus more on principles and understanding rather than on grunt-work calculations.

My main objection, and the reason for giving it 4 stars, is English language. I am not a native English speaker, and it's obvious that none of the authors is either. Even worse, I encounter at least one misleading, or hard to understand sentence per chapter (mostly among problems). The book most definitely needs proofreading and language corrections!

Great for learning if you're prepared
This book reads easily because it gives many concrete examples and uses a tutorial approach to teaching. However, you still need to know some math! You don't need a math degree. A good first course in calculus covering derivatives and integrals, including logs and exponentials, and some introductory combinatorics (basic knowledge of sets, permutations and combinations) is enough. Any sophomore or, at the latest, junior majoring in engineering or hard science has the prerequisites.

An understanding of probability is necessary for understanding statistics, so the first half of this book is probability. Without probability, statistics becomes something like "here are some facts, trust me, now here are some formulas, recipes and tables and you will learn when to use each one". For many people this may be enough, especially if they just need to get something done. But if you want to know why hypothesis testing is done the way it is and how it works, buy this book. For example, many statistics books just assume a normal distribution for sampling and the only thing you need to learn is when to use a one-tailed or two-tailed test and which formula to use. This is valid when working with sufficiently large populations or samples. In contrast, the worked example in this book does not use a normal distribution and it walks you through the reasoning and calculation. The reasoning is applicable to any population and distribution. When you change to a normal distribution the principles remain the same, only the formulas change. You learn the principles.

Now to the book's style. This is a tutorial style book that teaches using examples. It doesn't skip many steps and can feel somewhat chatty. It repeats simple calculations along the way so you don't have to page back and find where that number was calculated. This keeps the flow going. Learning by example is actually a good way to learn if you are new to the material. Some however, may not like this style, so read some online first before buying. If you already have probability under your belt and are up on your math then you may find this book slow going. This book is aimed at scientists and engineers, so if you are looking for a rigorous math book with proofs, look elsewhere.

Summary: If you've got the prerequisites then this is a great book for self teaching at a good price. If you are lacking in math and you need to do statistics now, then pick up a "cookbook" statistics book and come back later when you have the math background. If you know your stuff and need a reference, look elsewhere.

Starts understandable but becomes hard to read
I really don't feel qualified to rate this book because it quickly went over my head. The first chapters were an easy read and left me wanting to read more of the subject. After that, however the equations quickly progressed to where you may have to have a degree in mathematics to understand it. Not all the variables in the equations were defined, even in the index. This may be a very good text book if taught by someone knowledgeable in the subject, however I could not understand it by reading the book.