A proven textbook based on materials developed over the last decade to teach linear, generalized, and mixed model analysis to students of ecology, evolution, and environmental studies. While R is used throughout, the focus is firmly on statistical analysis.
Cox, D. R. and Oakes, D. (1984), Analysis of Survival Data, Chapman & Hall, London. Everitt, B. S. (1994), A Handbook of Statistical Analyses Using S-PLUS, Chapman & Hall, London. Hájek, J., Šidák, Z., and Sen, P. K. (1999), ...
The book includes more than 200 exercises with fully worked solutions. Some familiarity with basic statistical concepts, such as linear regression, is assumed. No previous programming experience is needed.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun.
After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.
The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis.
This is the first introductory statistics text to use an estimation approach from the start to help readers understand effect sizes, confidence intervals (CIs), and meta-analysis (‘the new statistics’).
The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject.
Even the most hesitant student is likely to embrace the material with this text." —David A.M. Peterson, Department of Political Science, Iowa State University Drawing on examples from across the social and behavioral sciences, Statistics ...
This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics.
A popular entry-level guide into the use of R as a statistical programming and data management language for students, post-docs, and seasoned researchers now in a new revised edition, incorporating the updates in the R environment, and also ...