To manage our environment sustainably, professionals must understand the quality and quantity of our natural resources. Statistical analysis provides information that supports management decisions and is universally used across scientific disciplines. Statistics in Natural Resources: Applications with R focuses on the application of statistical analyses in the environmental, agricultural, and natural resources disciplines. This is a book well suited for current or aspiring natural resource professionals who are required to analyze data and perform statistical analyses in their daily work. More seasoned professionals who have previously had a course or two in statistics will also find the content familiar. This text can also serve as a bridge between professionals who understand statistics and want to learn how to perform analyses on natural resources data in R.

The primary goal of this book is to learn and apply common statistical methods used in natural resources by using the R programming language. If you dedicate considerable time to this book, you will:

  • Develop analytical and visualization skills for investigating the behavior of agricultural and natural resources data.
  • Become competent in importing, analyzing, and visualizing complex data sets in the R environment.
  • Recode, combine, and restructure data sets for statistical analysis and visualization.
  • Appreciate probability concepts as they apply to environmental problems.
  • Understand common distributions used in statistical applications and inference.
  • Summarize data effectively and efficiently for reporting purposes.
  • Learn the tasks required to perform a variety of statistical hypothesis tests and interpret their results.
  • Understand which modeling frameworks are appropriate for your data and how to interpret predictions.
  • Includes over 130 exercises in R, with solutions available on the book’s website.

chapter 1|36 pages

Visualizing data

chapter 3|20 pages


chapter 6|10 pages

Inference for two-way tables

chapter 8|48 pages

Linear regression

chapter 9|20 pages

Multiple regression

chapter 10|24 pages

Analysis of variance

chapter 11|10 pages

Analysis of covariance

chapter 12|28 pages

Logistic regression

chapter 13|20 pages

Count regression

chapter 14|24 pages

Linear mixed models