Biostatistics with R : an introductory guide for field biologists / Jan Lepš, University of South Bohemia, Czech Republic, Petr Šmilauer, University of South Bohemia, Czech Republic.
By: Lepš, Jan [author.].
Contributor(s): Šmilauer, Petr [author.].
Publisher: Cambridge, UK ; New York, NY : Cambridge University Press, ©2020Description: 365 p.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781108480383; 9781108727341.Subject(s): Biometry | R (Computer program language)Genre/Form: Print books.Summary: "Biostatistics with R provides a straightforward introduction on how to analyse data from the wide field of biological research, including nature protection and global change monitoring. The book is centred around traditional statistical approaches, focusing on those prevailing in research publications. The authors cover t tests, ANOVA and regression models, but also the advanced methods of generalised linear models and classification and regression trees. Chapters usually start with several useful case examples, describing the structure of typical datasets and proposing research-related questions. All chapters are supplemented by example datasets and thoroughly explained, step-by-step R code demonstrating the analytical procedures and interpretation of results. The authors also provide examples of how to appropriately describe statistical procedures and results of analyses in research papers. This accessible textbook will serve a broad audience of interested readers, from students, researchers or professionals looking to improve their everyday statistical practice, to lecturers of introductory undergraduate courses"--| Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|
| On Shelf | QH323.5 .L46 2020 (Browse shelf) | Available | AU00000000016937 |
Includes index.
"Biostatistics with R provides a straightforward introduction on how to analyse data from the wide field of biological research, including nature protection and global change monitoring. The book is centred around traditional statistical approaches, focusing on those prevailing in research publications. The authors cover t tests, ANOVA and regression models, but also the advanced methods of generalised linear models and classification and regression trees. Chapters usually start with several useful case examples, describing the structure of typical datasets and proposing research-related questions. All chapters are supplemented by example datasets and thoroughly explained, step-by-step R code demonstrating the analytical procedures and interpretation of results. The authors also provide examples of how to appropriately describe statistical procedures and results of analyses in research papers. This accessible textbook will serve a broad audience of interested readers, from students, researchers or professionals looking to improve their everyday statistical practice, to lecturers of introductory undergraduate courses"--