Statistical Modelling in R
Series: Oxford Statistical Science Series; 35;
- Publisher's listprice GBP 78.00
-
35 217 Ft (33 540 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 10% (cc. 3 522 Ft off)
- Discounted price 31 695 Ft (30 186 Ft + 5% VAT)
Subcribe now and take benefit of a favourable price.
Subscribe
35 217 Ft
Availability
printed on demand
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Publisher OUP Oxford
- Date of Publication 29 January 2009
- ISBN 9780199219131
- Binding Paperback
- No. of pages592 pages
- Size 233x156x31 mm
- Weight 877 g
- Language English
- Illustrations 167 line illustrations, 1 halftone 0
Categories
Short description:
A comprehensive treatment of the theory of statistical modelling in R with an emphasis on applications to practical problems and an expanded discussion of statistical theory.
MoreLong description:
R is now the most widely used statistical package/language in university statistics departments and many research organisations. Its great advantages are that for many years it has been the leading-edge statistical package/language and that it can be freely downloaded from the R web site. Its cooperative development and open code also attracts many contributors meaning that the modelling and data analysis possibilities in R are much richer than in GLIM4, and so the R edition can be substantially more comprehensive than the GLIM4 edition of Statistical Modelling.
This text provides a comprehensive treatment of the theory of statistical modelling in R with an emphasis on applications to practical problems and an expanded discussion of statistical theory. A wide range of case studies is provided, using the normal, binomial, Poisson, multinomial, gamma, exponential and Weibull distributions, making this book ideal for graduates and research students in applied statistics and a wide range of quantitative disciplines.
Table of Contents:
Preface
Introducing R
Statistical modelling and inference
Regression and analysis of variance
Binary response data
Multinomial and Poisson response data
Survival data
Finite mixture models
Random effects models
Variance component models
Bibliography
R function and constant index
Dataset index
Subject index
