- Brossura: 119 pagine
- Editore: Oreilly & Associates Inc (7 luglio 2011)
- Lingua: Inglese
- ISBN-10: 1449307116
- ISBN-13: 978-1449307110
- Peso di spedizione: 249 g
Think Stats
e oltre 1.000.000 di libri sono disponibili per Amazon Kindle . Maggiori informazioni
| |||||||||||||||
Dettagli prodotto |
Recensioni clienti
Non ci sono ancora recensioni di clienti su Amazon.it
5 stelle
4 stelle
3 stelle
2 stelle
1 stella
Le recensioni clienti più utili su Amazon.com (beta)
Amazon.com:
4.2 su 5 stelle
9 recensioni
41 di 42 persone hanno trovato utile la seguente recensione
5.0 su 5 stelle
Learning Statistics using Programming
11 agosto 2011
Di CromsFury
- Pubblicato su Amazon.com
Formato:Brossura
If your grasp of Programming exceeds your understanding of Basic Statistics, this book IS for you. As a University Statistics professor, I am constantly looking for reading materials that I can use to integrate Practical Statistics with programming. I am generally faced with the problem of having to mine Programming texts for Stats lessons, all too often I am faced with books that attempt to teach a programming language with examples from Freshman Statistics as an afterthought. (Too much of one, not enough of the other)
This book comes at the problem from the other side. Given that you already have a healthy grasp on programming and are trying to learn Statistics, each topic is presented with helpful, real-world data examples, and a step-by-step explanation of how to code the solutions. That makes this book excellent supplementary material for a Statistics class, or at the very least, a wonderful refresher for those returning to Statistics, with programming in mind.
Caution:
This book is NOT for you if you do NOT have a basic understanding of Programming. This book will NOT teach you to program using statistics. It is meant to teach you statistics using programming.
This book comes at the problem from the other side. Given that you already have a healthy grasp on programming and are trying to learn Statistics, each topic is presented with helpful, real-world data examples, and a step-by-step explanation of how to code the solutions. That makes this book excellent supplementary material for a Statistics class, or at the very least, a wonderful refresher for those returning to Statistics, with programming in mind.
Caution:
This book is NOT for you if you do NOT have a basic understanding of Programming. This book will NOT teach you to program using statistics. It is meant to teach you statistics using programming.
23 di 24 persone hanno trovato utile la seguente recensione
5.0 su 5 stelle
100 pages and couple of hours to get a good flavor of Bayesian Stats
15 marzo 2012
Di Ravi Aranke
- Pubblicato su Amazon.com
Formato:Brossura
Bayesian statistics and Bayesian thinking has taken the world by storm. If you read Kahneman's popular
Thinking, Fast and Slow, you are advised to think in Bayesian terms viz. to adjust your prior beliefs in light of new evidence.
However, there is a big gulf between knowing what you should do and actually being able to do Bayesian statistics in a mathematically correct way. The language of probability and ability to manipulate the algebra of probability statements is a prerequisite and that has some steep learning curve.
Fortunately, thanks to Allen Downey, you are in luck if you know some python programming. (If not, just pick up a copy of Think Python: An Introduction to Software Design by the same author). The best part of this book is that is thin - running at just over 100 pages, you can work through it over a weekend. Better still, you can watch the author delivering an interactive seminar and just follow along. Search for 'Bayesian statistics made (as) simple (as possible)' on youtube.
When he says that it is Bayesian Statistics made as simple as possible, that is no exaggeration.
As some of the reviewers have mentioned, Allen Downey has kindly made this book, as well as few other books, freely available on his site. Hats off to you, Sir!
Thinking, Fast and Slow, you are advised to think in Bayesian terms viz. to adjust your prior beliefs in light of new evidence.
However, there is a big gulf between knowing what you should do and actually being able to do Bayesian statistics in a mathematically correct way. The language of probability and ability to manipulate the algebra of probability statements is a prerequisite and that has some steep learning curve.
Fortunately, thanks to Allen Downey, you are in luck if you know some python programming. (If not, just pick up a copy of Think Python: An Introduction to Software Design by the same author). The best part of this book is that is thin - running at just over 100 pages, you can work through it over a weekend. Better still, you can watch the author delivering an interactive seminar and just follow along. Search for 'Bayesian statistics made (as) simple (as possible)' on youtube.
When he says that it is Bayesian Statistics made as simple as possible, that is no exaggeration.
As some of the reviewers have mentioned, Allen Downey has kindly made this book, as well as few other books, freely available on his site. Hats off to you, Sir!
14 di 15 persone hanno trovato utile la seguente recensione
4.0 su 5 stelle
Good Book - Free from Authors Site
28 gennaio 2012
Di Devin R
- Pubblicato su Amazon.com
Formato:Formato Kindle
This is a good book to teach programmers [python especially] how to use mathematical statistics in their programs. The only real shame about the Kindle version of this book is it is available for free under the creative commons from the publisher, Greenteapress, for free but it's being sold here for a 10 spot.
›
Vai su Amazon.com per consultare tutte le 9 recensioni
4.2 su 5 stelle
