Statistics in Natural Resources

This book focuses on the application of statistical analyses in the environmental, agricultural, and natural resources disciplines.

Auteur

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 over 70 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.

Texte du rabat

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 that are required to analyze data and perform statistical analyses in their daily work. More seasoned professionals that have previously had a course or two in statistics will also find the content familiar. This book can also serve as a bridge between professionals that 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.

Contenu

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