Stock photo: cover may vary
Data Analysis Using Regression and Multilevel/Hierarchical Models Paperback - 2006 - 1st Edition
by Andrew Gelman
- New
- Paperback
- first
A$67.90
Free Delivery within USA
Standard delivery: 5 to 10 days
More delivery options
Standard delivery: 5 to 10 days
Dropship order
Ships from Ergodebooks (Texas, United States)
Details
- Title Data Analysis Using Regression and Multilevel/Hierarchical Models
- Author Andrew Gelman
- Binding Paperback
- Edition number 1st
- Edition 1
- Condition New
- Pages 648
- Volumes 1
- Language ENG
- Publisher Cambridge University Press, Cambridge
- Publication date 2006-12-18
- Features Bibliography, Index, Table of Contents
- Bookseller's Inventory # DADAX052168689X
- ISBN 9780521686891 / 052168689X
- Weight 2.65 lbs (1.20 kg)
- Dimensions 9.9 x 6.9 x 1.4 in (25.15 x 17.53 x 3.56 cm)
- Size 6.97x1.38x9.96
- Category Politics / Current Events
- Library of Congress subjects Regression analysis, Multilevel models (Statistics)
- Library of Congress Catalogue Number 2006040566
- Dewey Decimal Code 519.536
- Quantity available 6
About Ergodebooks Texas, United States
Biblio member since 2005
Our goal is to provide best customer service and good condition books for the lowest possible price. We are always honest about condition of book. We list book only by ISBN # and hence exact book is guaranteed.
We have 30 day return policy.
Reader reviews for Data Analysis Using Regression and Multilevel/Hierarchical Models
Write a review for this book
Important Terms and Guidelines
- Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
- Reviews must be at least 140 characters in length.
- Please do not reveal critical plot elements.
- This is not a help line. Contact customer support if you need help.
Your review must not include:
- Obscenities, discriminatory language, or other insulting language not suitable for public domain
- Advertisements, “spam” content, or references to other products, offers or websites.
- Email addresses, URLs, phone numbers, physical addresses or other contact information.
- Overly critical comments about other reviews or reviewers
- Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
- Availability, price, or alternative ordering/shipping information
