| Description |
1 online resource (xvi, 313 pages) : illustrations (some color) |
| Contents |
Introduction to R -- Before You Begin (aka Thoughts on Proper Data Analysis) -- Exploratory Data Analysis and Data Summarization -- Introduction to Plotting -- Basic Statistical Analyses -- More Linear Models in R! -- Generalized Linear Models (GLM) -- Mixed Effects Models -- Advanced Data Wrangling and Plotting -- Writing Loops and Functions in R -- Final Thoughts |
| Summary |
Whether at the undergraduate, graduate, or post-graduate level, Applied Statistics with R: A Practical Guide for the Life Sciences teaches readers to properly analyze data in an efficient, accessible, plainspoken, frank, and occasionally humorous manner. Readers will come away with the knowledge of which analyses they should use and when they should use them, an important skill in an age when the statistical analyses used in the life-sciences are becoming increasingly advanced. This 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. Written around a single real-world dataset, Applied Statistics with R which encourages readers to become deeply familiar with an imperfect but realistic set of data, much like they themselves might collect. Early chapters are designed to teach basic data manipulation skills and build good habits in preparation for learning more advanced analyses. This approach also 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, or computational biology. -- Provided by the publisher |
| Notes |
Includes index |
|
Online resource; title from PDF title page (Oxford Scholarship Online, viewed February 11, 2022) |
| Subject |
Biometry.
|
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R (Computer program language)
|
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Biometry
|
|
biometrics.
|
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Biometry
|
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R (Computer program language)
|
| Form |
Electronic book
|
| ISBN |
9780192640123 |
|
0192640127 |
|
9780191905537 |
|
0191905534 |
|
