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Modern Statistics for Modern Biology
SPAR
kr 187
Modern Statistics for Modern Biology
If you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book. You can visualize and analyze your own data, apply unsupervised and supervised learning, integrate datasets, apply hypothesis testing, and make publication-quality figures using the power of R/Bioconductor and ggplot2. This book will teach you ''cooking from scratch'', from raw data to beautiful illuminating output, as you learn to write your own scripts in the R language and to use advanced statistics packages from CRAN and Bioconductor. It covers a broad range of basic and advanced topics important in the analysis of high-throughput biological data, including principal component analysis and multidimensional scaling, clustering, multiple testing, unsupervised and supervised learning, resampling, the pitfalls of experimental design, and power simulations using Monte Carlo, and it even reaches networks, trees, spatial statistics, image data, and microbial ecology. Using a minimum of mathematical notation, it builds understanding from well-chosen examples, simulation, visualization, and above all hands-on interaction with data and code.
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If you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book. You can visualize and analyze your own data, apply unsupervised and supervised learning, integrate datasets, apply hypothesis testing, and make publication-quality figures using the power of R/Bioconductor and ggplot2. This book will teach you ''cooking from scratch'', from raw data to beautiful illuminating output, as you learn to write your own scripts in the R language and to use advanced statistics packages from CRAN and Bioconductor. It covers a broad range of basic and advanced topics important in the analysis of high-throughput biological data, including principal component analysis and multidimensional scaling, clustering, multiple testing, unsupervised and supervised learning, resampling, the pitfalls of experimental design, and power simulations using Monte Carlo, and it even reaches networks, trees, spatial statistics, image data, and microbial ecology. Using a minimum of mathematical notation, it builds understanding from well-chosen examples, simulation, visualization, and above all hands-on interaction with data and code.
Produktdetaljer
Sprog: Engelsk
Sider: 402
ISBN-13: 9781108705295
Indbinding: Paperback
Udgave:
ISBN-10: 1108705294
Udg. Dato: 28 feb 2019
Længde: 16mm
Bredde: 279mm
Højde: 218mm
Oplagsdato: 28 feb 2019
Forfatter(e) Wolfgang Huber, Susan (Stanford University Holmes, Susan Holmes


Kategori Sandsynlighedsregning og statistik


Sprog Engelsk


Indbinding Paperback


Sider 402


Udgave


Længde 16mm


Bredde 279mm


Højde 218mm

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