경문사

쇼핑몰 >  수입도서 >  Mathematics >  Statistics

A Modern Introduction to Probability and Statistics(2005)  무료배송

 
지은이 : F.M. Dekking, C. Kraaikamp, H.P. Lopuha�, L.E. Meester
출판사 : Springer-Verlag
판수 : 1 edition
페이지수 : 488 pages
ISBN : 1852338962
예상출고일 : 입금확인후 2일 이내
주문수량 :
도서가격 : 품절
     

 
Probability and Statistics are studied by most science students. 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 show how the fundamentals of probabilistic and statistical theories arise intuitively. A Modern Introduction to Probability and Statistics has numerous quick exercises to give direct feedback to students. In addition there are over 350 exercises, half of which have answers, of which half have full solutions. A website gives access to the data files used in the text, and, for instructors, the remaining solutions. The only pre-requisite 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 modern methods such as the bootstrap.

Why Probability and Statistics?- Outcomes, Events and Probability.- Conditional Probability and Independence.- Discrete Random Variables.- Continuous Random Variables.- Simulation.- Expectation and Variance.- Computations with Random Variables.- Joint Distributions and Independence.- Covariance and Correlation.- More Computations with More Random Variables.- The Poisson Process.- The Law of Large Numbers.- The Central Limit Theorem.- Exploratory Data Analysis: Graphical Summaries.- Exploratory Data Analysis: Numerical Summaries.- Basic Statistical Models.- The Bootstrap.- Unbiased Estimators.- Efficiency and Mean Squared Error.- Maximum Likelihood.- The Method of Least Squares.- Confidence Intervals for the Mean.- More on Confidence Intervals.- Testing Hypotheses: Essentials.- Testing Hypotheses: Elaboration.- The t-test.- Comparing Two Samples.- Datasets.- Appendix A: Answers to Selected Exercises.- Appendix B: Solutions to Selected Exercises.- References.- Index.


"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."


"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!"
Introduction to Partial Di...
-Zachmanoglou-
 
 
Real Analysis Modern Techn...
-Folland-
 
 
A First Course in Abstract...
-John B. Fraleigh-
 
 
   
 
플립러닝을 위한 대수학...
갈루아 증명
성균관대학교 access co...
Applied Statistics...
Introductory Stati...
Applied Statistics...
Applied Statistics...
Introduction to Ma...