A01=Benjamin M. Bolker
Accuracy and precision
Algorithm
Analysis of variance
Author_Benjamin M. Bolker
Bayesian
Bayesian inference
Bayesian statistics
Beta-binomial distribution
Binomial distribution
Calculation
Category=PBWH
Category=PSAF
Combination
Confidence interval
Covariate
Credible interval
Curve fitting
Data set
Derivative
Ecology
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eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
Equation
Estimation
Estimator
Expected value
Exponential function
Frequentist inference
Functional response
Gamma distribution
Histogram
Inference
Likelihood function
Likelihood-ratio test
Linear regression
Logarithm
Logistic function
Logit
Markov chain Monte Carlo
Maxima and minima
Maximum likelihood estimation
Model selection
Negative binomial distribution
Nelder-Mead method
Normal distribution
Null hypothesis
Observational error
Overdispersion
P-value
Parameter
Parameter (computer programming)
Parametrization
Poisson distribution
Posterior probability
Predation
Prediction
Prior probability
Probability
Probability distribution
Proportionality (mathematics)
Quantity
Random variable
Realization (probability)
Scale parameter
Shape parameter
Special case
Square root
Standard deviation
Statistic
Statistical significance
Statistics
Stochastic
Summation
Variable (mathematics)
Variance
Product details
- ISBN 9780691125220
- Weight: 992g
- Dimensions: 178 x 254mm
- Publication Date: 21 Jul 2008
- Publisher: Princeton University Press
- Publication City/Country: US
- Product Form: Hardback
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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
Benjamin M. Bolker is a theoretical ecologist in the departments of Mathematics & Statistics and Biology at McMaster University.
