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.
List of Tables
List of Figures
Preface
Chapter 1 Visualizing Data
Chapter 2 Summary statistics and distributions
Chapter 3 Probability
Chapter 4 Hypothesis tests for means and variances
Chapter 5 Inference for counts and proportions
Chapter 6 Inference for two-way tables
Chapter 7 Sample size and statistical power
Chapter 8 Linear regression
Chapter 9 Multiple regression
Chapter 10 Analysis of variance
Chapter 11 Analysis of covariance
Chapter 12 Logistic regression
Chapter 13 Count regression
Chapter 14 Linear mixed models
Chapter 15 Communicating statistical results with visualizations
Appendix
Biography
Matthew Russell is a forest analytics consultant at Arbor Custom Analytics LLC where he uses data to solve natural resources problems. He is the author/co-author of more than 75 peer-reviewed publications focused on applied forestry research. He has conducted extensive research and teaching on topics related to forest modeling and statistics. He regularly offers short courses and workshops on data science and R for natural resources and environmental professionals.
