| Author | Justin C Touchon |
| Language | English |
| ISBN | 9780197797358 |
| Publisher | Oxford University Press |
| Pages | 296 |
Size | 232 x 154 mm |
| Format | Paperback |
| Images | Illustrations |
| Year published | 2e editie 2025 |
Applied Statistics with R:
- Teaches the most commonly used analyses needed for any life scientist
- Uses a single large real dataset instead of many previously cleaned 'perfect' datasets
- Focuses on teaching readers to understand what their data look like and letting that guide their analyses
Over the past few decades, the life sciences have experienced an analytical revolution. With powerful computers now widely available, students and researchers are expected to perform increasingly advanced statistical analyses—previously the domain of only statistical experts. However, most undergraduate and graduate students still lack the proper formal training needed to succeed. Regardless of your experience level, Applied Statistics with R will teach you in an efficient, accessible, and occasionally humorous manner, ensuring you understand how and when to perform your analyses.
The book uses the statistical language R, which is the choice of ecologists worldwide and is rapidly becoming the 'go-to' stats program throughout the life sciences. By using a single, real-world dataset throughout the book, readers will become deeply familiar with an imperfect but realistic set of data. Early chapters are designed to teach basic data manipulation skills and build good habits in preparation for learning more advanced analyses. This approach demonstrates the importance of viewing data through different lenses, facilitating an easy and natural progression from linear and generalized linear models through to mixed effects versions of those same analyses. Readers will also learn advanced plotting and data-wrangling techniques and gain an introduction to writing their own functions.
Applied Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners throughout the life sciences, whether in the fields of ecology, evolution, environmental studies, psychology, or neuroscience.
Contents
Preface
1 Introduction to R
2 Before You Begin (aka Thoughts on Proper Data Analysis)
3 Exploratory Data Analysis and Data Summarization
4 Introduction to Plotting
5 Basic Statistical Analyses
6 More Linear Models!
7 Generalized Linear Models
8 Mixed Effects Models
9 Other useful models you should know about
10 Advanced Data Wrangling and Plotting
11 Writing Loops and Functions in R
12 Final Thoughts
Index
