| DC Field | Value | Language |
|---|
| dc.contributor.author | Dekking, F.M. | - |
| dc.contributor.author | Kraaikamp, C. | - |
| dc.contributor.author | Lopuhaä, H P | - |
| dc.date.accessioned | 2021-04-19T04:12:31Z | - |
| dc.date.available | 2021-04-19T04:12:31Z | - |
| dc.date.issued | 2005 | - |
| dc.identifier.isbn | 978-1-85233-896-1 | - |
| dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/56 | - |
| dc.description | With the exception of the first one, chapters in this book consist of three main
parts. First, about four sections discussing new material, interspersed with a
handful of so-called Quick exercises. Working these—two-or-three-minute—
exercises should help to master the material and provide a break from reading
to do something more active. On about two dozen occasions you will find
indented paragraphs labeled Remark, where we felt the need to discuss more
mathematical details or background material. These remarks can be skipped
without loss of continuity; in most cases they require a bit more mathematical
maturity. Whenever persons are introduced in examples we have determined
their sex by looking at the chapter number and applying the rule “He is odd,
she is even.” Solutions to the quick exercises are found in the second to last
section of each chapter.
The last section of each chapter is devoted to exercises, on average thirteen
per chapter. For about half of the exercises, answers are given in Appendix C,
and for half of these, full solutions in Appendix D. Exercises with both a
short answer and a full solution are marked with and those with only a
short answer are marked with (when more appropriate, for example, in
“Show that . . . ” exercises, the short answer provides a hint to the key step).
Typically, the section starts with some easy exercises and the order of the
material in the chapter is more or less respected. More challenging exercises
are found at the end.
Much of the material in this book would benefit from illustration with a
computer using statistical software. A complete course should also involve
computer exercises. Topics like simulation, the law of large numbers, the
central limit theorem, and the bootstrap loudly call for this kind of experi-
ence. For this purpose, all the datasets discussed in the book are available at
http://www.springeronline.com/1-85233-896-2. The same Web site also pro-
vides access, for instructors, to a complete set of solutions to the exercises;
go to the Springer online catalog or contact textbooks@springer-sbm.com to
apply for your password. | en_US |
| dc.description.abstract | Probability and statistics are fascinating subjects on the interface between
mathematics and applied sciences that help us understand and solve practical
problems. We believe that you, by learning how stochastic methods come
about and why they work, will be able to understand the meaning of statistical
statements as well as judge the quality of their content, when facing such
problems on your own. Our philosophy is one of how and why: instead of just
presenting stochastic methods as cookbook recipes, we prefer to explain the
principles behind them.
In this book you will find the basics of probability theory and statistics. In
addition, there are several topics that go somewhat beyond the basics but
that ought to be present in an introductory course: simulation, the Poisson
process, the law of large numbers, and the central limit theorem. Computers
have brought many changes in statistics. In particular, the bootstrap has
earned its place. It provides the possibility to derive confidence intervals and
perform tests of hypotheses where traditional (normal approximation or large
sample) methods are inappropriate. It is a modern useful tool one should learn
about, we believe.
Examples and datasets in this book are mostly from real-life situations, at
least that is what we looked for in illustrations of the material. Anybody who
has inspected datasets with the purpose of using them as elementary examples
knows that this is hard: on the one hand, you do not want to boldly state
assumptions that are clearly not satisfied; on the other hand, long explanations
concerning side issues distract from the main points. We hope that we found
a good middle way. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Springer | en_US |
| dc.subject | Commerce | en_US |
| dc.subject | Statistics | en_US |
| dc.subject | A Modern Introduction | en_US |
| dc.title | A Modern Introduction to Probability and Statistics | en_US |
| dc.title.alternative | Understanding Why and How | en_US |
| dc.type | Book | en_US |
| Appears in Collections: | ARTS & SCIENCE
|
