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Full Description
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.
Contents
1 Visualizing Data 2 Summary statistics and distributions 3 Probability 4 Hypothesis tests for means and variances 5 Inference for counts and proportions 6 Inference for two-way tables 7 Sample size and statistical power 8 Linear regression 9 Multiple regression 10 Analysis of variance 11 Analysis of covariance 12 Logistic regression 13 Count regression 14 Linear mixed models 15 Communicating statistical results with visualizations
