The lnfluence Function. Monte Cado and Bayesian Analyses. Linear Regression Using Bayesian Methods. Other Kinds of Regression Analyses. Robust Regression. Quantile Regression. Logistic Regression. Non-Linear Regression. Multiple Regression. Path AnaIysis. Model Selection Cri teria. Model Selection Methods for Multiple Regression. Model Selection Methods in Path Analysis. Bayesian Model Selection. Constructing F- Ratios. Randomized Block. Understanding the lnteraction Term. Comparing Means. A Posteriori Comparisons.
A Priori Contrasts. Bonferroni Corrections and the Problem of Multiple Tests. Chapter The Analysis of Categorical Data. Two- Way Contingency Tables. Organizing the Data. Are the Variables lndependent? Testing the Hypothesis: Pearson's Chi-square Test. Which Test To Choose? Multi- Way Contingency Tables.
On to Multi- Way Tables! Bayesian Approaches to Contingency Tables. Tests for Goodness-of-Fit. Goodness-of- Fit Tests for Discrete Distributions. Testing Goodness-of-Fit for Continuous. Distributions: The Kolmogorov-Smirnov Test. Approaching Multivariate Data.
The Need for Matrix Algebra. Comparing Multivariate Means. The Multivariate Normal Distribution. Testing for Multivariate Normality. Measurements of Multivariate Distance. Measuring Distances between Two IndividuaIs. Measuring Distances Between Two Groups. Other Measurements of Distance. Principal Component Analysis Factor Analysis. Principal Coordinates Analysis. Correspondence Analysis. Non-Metric Multidimensional Scaling. Advantages and Disadvantages of Ordination.
Cluster Analysis. Choosing a Clustering Method. Discriminant Analysis. Advantages and Disadvantages of Classification. Multivariate Multiple Regression. Redundancy Analysis. Includes all testable terms, concepts, persons, places, and events.
Cram Just the FACTS studyguides gives all of the outlines, highlights, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram is Textbook Specific. Accompanies: This item is printed on demand. This book covers basic concepts in population and quantitative genetics, including measuring selection on phenotypic traits.
The emphasis is on material applicable to field studies of evolution focusing on ecologically important traits. Topics addressed are critical for training students in ecology, evolution, conservation biology, agriculture, forestry, and wildlife management. Many texts in this field are too complex and mathematical to allow the average beginning student to readily grasp the key concepts. A Primer of Ecological Genetics, in contrast, employs mathematics and statistics-fully explained, but at a less advanced level-as tools to improve understanding of biological principles.
The main goal is to enable students to understand the concepts well enough that they can gain entry into the primary literature. Integration of the different chapters of the book shows students how diverse concepts relate to each other. Virtually all testable terms, concepts, persons, places, and events are included. Cram Textbook Outlines gives all of the outlines, highlights, notes for your textbook with optional online practice tests.
Only Cram Outlines are Textbook Specific. Cram is NOT the Textbook. Accompanys: ". Provides simple explanations of the important concepts in population and community ecology. Provides R code throughout, to illustrate model development and analysis, as well as appendix introducing the R language. Interweaves ecological content and code so that either stands alone. Supplemental web site for additional code.
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand.
Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians.
It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works.
It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals. This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management. Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticians Covers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and more Deemphasizes computer coding in favor of basic principles Explains how to write out properly factored statistical expressions representing Bayesian models.
A groundbreaking approach to scale and scaling in ecological theory and practice Scale is one of the most important concepts in ecology, yet researchers often find it difficult to find ecological systems that lend themselves to its study. Scaling in Ecology with a Model System synthesizes nearly three decades of research on the ecology of Sarracenia purpurea—the northern pitcher plant—showing how this carnivorous plant and its associated food web of microbes and macrobes can inform the challenging question of scaling in ecology.
Drawing on a wealth of findings from their pioneering lab and field experiments, Aaron Ellison and Nicholas Gotelli reveal how the Sarracenia microecosystem has emerged as a model system for experimental ecology.
Ellison and Gotelli examine Sarracenia at a hierarchy of spatial scales—individual pitchers within plants, plants within bogs, and bogs within landscapes—and demonstrate how pitcher plants can serve as replicate miniature ecosystems that can be studied in wetlands throughout the United States and Canada.
They show how research on the Sarracenia microecosystem proceeds much more rapidly than studies of larger, more slowly changing ecosystems such as forests, grasslands, lakes, or streams, which are more difficult to replicate and experimentally manipulate. Scaling in Ecology with a Model System offers new insights into ecophysiology and stoichiometry, demography, extinction risk and species distribution models, food webs and trophic dynamics, and tipping points and regime shifts.
Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R connects applied statistics to the environmental and ecological fields. It follows the general approach to solving a statistical modeling problem, covering model specification, parameter estimation, and model evaluation.
The author uses many examples to illustrate the statistical models and presents R implementations of the models. The book first builds a foundation for conducting a simple data analysis task, such as exploratory data analysis and fitting linear regression models.
It then focuses on statistical modeling, including linear and nonlinear models, classification and regression tree, and the generalized linear model. The text also discusses the use of simulation for model checking, provides tools for a critical assessment of the developed model, and explores multilevel regression models, which are a class of models that can have a broad impact in environmental and ecological data analysis.
Based on courses taught by the author at Duke University, this book focuses on statistical modeling and data analysis for environmental and ecological problems. By guiding readers through the processes of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical model. This handbook focuses on the enormous literature applying statistical methodology and modelling to environmental and ecological processes.
The 21st century statistics community has become increasingly interdisciplinary, bringing a large collection of modern tools to all areas of application in environmental processes. In addition, the environmental community has substantially increased its scope of data collection including observational data, satellite-derived data, and computer model output. The resultant impact in this latter community has been substantial; no longer are simple regression and analysis of variance methods adequate.
The contribution of this handbook is to assemble a state-of-the-art view of this interface. Features: An internationally regarded editorial team. A distinguished collection of contributors. A thoroughly contemporary treatment of a substantial interdisciplinary interface. Written to engage both statisticians as well as quantitative environmental researchers.
Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R, Second Edition, connects applied statistics to the environmental and ecological fields. Using examples from published works in the ecological and environmental literature, the book explains the approach to solving a statistical problem, covering model specification, parameter estimation, and model evaluation.
It includes many examples to illustrate the statistical methods and presents R code for their implementation. The emphasis is on model interpretation and assessment, and using several core examples throughout the book, the author illustrates the iterative nature of statistical inference.
The book starts with a description of commonly used statistical assumptions and exploratory data analysis tools for the verification of these assumptions. It then focuses on the process of building suitable statistical models, including linear and nonlinear models, classification and regression trees, generalized linear models, and multilevel models. It also discusses the use of simulation for model checking, and provides tools for a critical assessment of the developed models. Qian — in Mathematics.
Author : Song S. Qian File Size : Gelfand — in Mathematics. Author : Alan E. Gelfand File Size : Guthery — in Nature. Author : Fred S. Guthery File Size : Author : Gregg Hartvigsen File Size : The Interpretation of Ecological Data E. Pielou — in Science. Author : E. Pielou File Size : Everitt — in Medical. Author : Brian S. Everitt File Size : Ecological Statistics Gordon A. Fox — in Ecology. Author : Gordon A. Fox File Size : Bakus — in Science. Author : Gerald J. Bakus File Size :
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