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Ecological Models and Data in R

Ecological Models and Data in R
Author: Benjamin M. Bolker
Publisher: Princeton University Press
Category: Book

List Price: $55.00
Buy New: $37.12
You Save: $17.88 (33%)



New (12) Used (1) from $37.12

Sales Rank: 38739

Media: Hardcover
Edition: 508
Number Of Items: 1
Pages: 408
Shipping Weight (lbs): 2.2
Dimensions (in): 10.1 x 7 x 1.1

ISBN: 0691125228
Dewey Decimal Number: 577
EAN: 9780691125220
ASIN: 0691125228

Publication Date: July 21, 2008
Shipping: Eligible for Super Saver Shipping
Availability: Usually ships in 24 hours

Also Available In:

  • Paperback - Ecological Models and Data in R

Similar Items:

  • Statistical Computation for Environmental Sciences in R: Lab Manual for Models for Ecological Data (Lab Manual)
  • Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities
  • The R Book
  • Bayesian Computation with R (Use R)
  • Data Analysis Using Regression and Multilevel/Hierarchical Models

Editorial Reviews:

Product Description

Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language R. Drawing on extensive experience teaching these techniques to graduate students in ecology, Benjamin Bolker shows how to choose among and construct statistical models for data, estimate their parameters and confidence limits, and interpret the results. The book also covers statistical frameworks, the philosophy of statistical modeling, and critical mathematical functions and probability distributions. It requires no programming background--only basic calculus and statistics.

  • Practical, beginner-friendly introduction to modern statistical techniques for ecology using the programming language R
  • Step-by-step instructions for fitting models to messy, real-world data
  • Balanced view of different statistical approaches
  • Wide coverage of techniques--from simple (distribution fitting) to complex (state-space modeling)
  • Techniques for data manipulation and graphical display
  • Companion Web site with data and R code for all examples


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