Buch, Englisch, 352 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 557 g
Reihe: Use R!
An Introduction to Statistics Through Biological Data
Buch, Englisch, 352 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 557 g
Reihe: Use R!
ISBN: 978-1-4614-1301-1
Verlag: Springer
Biostatistics with R is designed around the dynamic interplay among statistical methods, their applications in biology, and their implementation. The book explains basic statistical concepts with a simple yet rigorous language. The development of ideas is in the context of real applied problems, for which step-by-step instructions for using R and R-Commander are provided. Topics include data exploration, estimation, hypothesis testing, linear regression analysis, and clustering with two appendices on installing and using R and R-Commander. A novel feature of this book is an introduction to Bayesian analysis.
This author discusses basic statistical analysis through a series of biological examples using R and R-Commander as computational tools. The book is ideal for instructors of basic statistics for biologists and other health scientists. The step-by-step application of statistical methods discussed in this book allows readers, who are interested in statistics and its application in biology, to use the book as a self-learning text.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
- Naturwissenschaften Biowissenschaften Angewandte Biologie Biomathematik
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
Weitere Infos & Material
Introduction.- Data Exploration.- Exploring Relationships.- Probability.- Random Variables and Probability Distribtions.- Estimation.- Hypothesis Testing.- Testing a Hypothesis on the Relatinoship Between Two Variables.- Analysis of Variance.- Analysis of Categorial Variables.- Regression Analysis.- Clustering.- Bayesian Analysis.