Randomization, Bootstrap And Monte Carlo Methods In Biology, Third Edition Manly, Bryan F.j.
Click Here ->>> https://geags.com/2twcEl
Review of Randomization, Bootstrap and Monte Carlo Methods in Biology, Third Edition by Bryan F.J. Manly
Randomization, Bootstrap and Monte Carlo Methods in Biology is a textbook that introduces computational statistics techniques for analyzing biological data. The book covers three main topics: randomization, bootstrapping, and Monte Carlo methods of inference. The author, Bryan F.J. Manly, is a distinguished professor of statistics and ecology at the University of Otago, New Zealand.
The book is aimed at advanced undergraduate and graduate students in biology, ecology, environmental science, and related fields. It assumes some basic knowledge of statistics and probability, but does not require any programming skills. The book provides clear explanations of the concepts and methods, along with examples and exercises using real biological data sets. The book also includes a CD-ROM with software and data files for implementing the methods in R, SAS, and Excel.
The book is divided into 14 chapters. The first chapter gives an overview of the role of computational statistics in biology and the advantages of randomization, bootstrapping, and Monte Carlo methods over traditional parametric methods. The second chapter introduces randomization tests for comparing two or more groups or treatments, using permutation and rank-based methods. The third chapter extends the randomization tests to more complex designs, such as factorial experiments, repeated measures, and nested designs. The fourth chapter covers bootstrapping methods for estimating confidence intervals and standard errors for various statistics, such as means, medians, correlations, and regression coefficients. The fifth chapter explains how to use bootstrapping for testing hypotheses and comparing models. The sixth chapter introduces Monte Carlo methods for simulating data from various probability distributions and generating random samples. The seventh chapter shows how to use Monte Carlo methods for estimating parameters and testing hypotheses for complex models that are difficult or impossible to analyze by conventional methods. The eighth chapter discusses some special topics in Monte Carlo methods, such as Markov chain Monte Carlo (MCMC), importance sampling, and Bayesian inference. The ninth chapter reviews some basic concepts and techniques in linear regression analysis and shows how to use randomization, bootstrapping, and Monte Carlo methods for regression problems. The tenth chapter covers nonlinear regression models and generalized linear models (GLMs), such as logistic regression and Poisson regression. The eleventh chapter deals with multivariate analysis techniques, such as principal component analysis (PCA), cluster analysis, discriminant analysis, and canonical correlation analysis. The twelfth chapter introduces some advanced topics in multivariate analysis, such as multidimensional scaling (MDS), correspondence analysis (CA), and ordination methods. The thirteenth chapter focuses on spatial statistics methods, such as geostatistics, spatial autocorrelation, spatial point patterns, and spatial interpolation. The fourteenth chapter covers some applications of computational statistics in molecular biology, such as phylogenetic analysis, DNA sequence analysis, microarray analysis, and genome-wide association studies (GWAS).
The book is well-written and organized. It provides a comprehensive and accessible introduction to randomization, bootstrapping, and Monte Carlo methods for biological data analysis. It illustrates the practical applications of these methods with relevant examples and exercises from various fields of biology. It also provides useful software tools and data sets for implementing the methods in R, SAS, and Excel. The book is suitable for both self-study and classroom use.
Randomization, Bootstrap and Monte Carlo Methods in Biology is a valuable resource for students and researchers who want to learn more about computational statistics techniques for analyzing biological data. It is a must-read for anyone who wants to apply these methods to their own research problems. aa16f39245
