Online Books

Statistical Inference via Data Science (A ModernDive into R and the Tidyverse)

  • Introduction to R, RStudio and the Tidyverse for visualizing, wrangling and analyzing your data.

Applied Statistics for Experimental Biology by Jeffrey A. Walker

  • (Primary text we follow for BIO 5100/L) Takes a statistical modeling approach, focusing on estimates of effects and uncertainty instead of traditional hypothesis testing.

R for Data Science (2nd Edition) by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund

  • Mostly covers details of using the tidyverse to import, wrangle and get your data ready to analyze.

Spatial Statistics for Data Science: Theory and Practice with R by Paula Moraga

  • Describes statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R. Includes an overview of types of spatial data, relevant R packages for analyzing and visualizing spatial data, explanations of spatial statistics, and fully reproducible examples analyzing areal, geostatistical, and point pattern data.

Guide to Effect Sizes and Confidence Intervals

  • Collaborative guide aims to provide academics, students and researchers with hands-on, step-by-step instructions for calculating effect sizes and confidence intervals for common statistical procedures.

Fundamentals of Data Visualization (using R) by Claus O. Wilke

  • A guide to making visualizations that accurately reflect the data, tell a story, and look professional.

  • The R code for the book can be found on GitHub, at https://github.com/clauswilke/dataviz

Analysis of community ecology data in R by David Zelený

  • Multivariate analysis including ordination (e.g., PCA, nMDS plots), cluster analysis and diversity analysis

Applied Biostats by Yaniv Brandvain

  • A “book” written to accompany his biostats course, lots of good guidance and resources.

Statistical Thinking for the 21st Century by Russell A. Poldrack

  • Modern approach to introductory statistics, good sections on plotting and summarizing data.

Introduction to Modern Statistics by Mine Çetinkaya-Rundel & Johanna Hardin

  • Modern take on introductory statistics in an R based format.

Modern Statistics for Modern Biology by Susan Holmes, Wolfgang Huber

  • Covers many modern approaches to data analysis, and many different types of data including RNA-Seq, flow-cytometry, taxa abundances, imaging data and single cell measurements.

  • Note some of the R code relies on Base R instead of Tidyverse functions.

Beyond Multiple Linear Regression: Generalized Linear Models and Multilevel Models By Julie Legler and Paul Roback

  • More advanced stats topics including Generalized Linear Models likelihood theory, zero-inflated Poisson, and parametric bootstrapping

Biological Statistics by John H. McDonald

  • Traditional statistics text, covering details of specific hypothesis tests (not R based, but potentially useful if your adviser makes you to use old school statistics)