Ecological Models and Data in R Review

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Ecological Models and Data in R ReviewThis book, in part, was developed from Dr. Bolker's graduate course in Ecological Models and Data at the University of Florida. This was the best course I took as a graduate student, it transformed the set of quantitative tools I was able to bring to bear on ecological questions. There was so much worthwhile material covered in this class that I took it twice (UF only counted the first time:). Since graduate school I still frequently refer to my notes from the class. With the publication of "Ecological Models and Data in R" even those who didn't have the good fortune of being in Bolker's class can learn approaches for integrating ecological theory and data. Bolker's book covers much of the material from his course and thus is an excellent resource for graduate students and faculty alike.Ecological Models and Data in R Overview
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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