Statistical Modeling With R A Dual Frequentist and Bayesian Approach for Life Scientists

An accessible textbook that explains, discusses, and applies both the frequentist and Bayesian theoretical frameworks to fit the different types of statistical models that allow an analysis of the types of data most commonly gathered by life scientists.

A propos de l'auteur

Pablo Inchausti is Professor of Ecology at the Universidad de la República, Centro Universitario Regional del Este, Uruguay. He is the co-editor of the influential and highly-cited book Biodiversity and Ecosystem Functioning: synthesis and perspectives (OUP, 2002) and has been successfully teaching statistics and mathematical modelling to students of the life and social sciences for over 15 years.

Résumé

An accessible textbook that explains, discusses, and applies both the frequentist and Bayesian theoretical frameworks to fit the different types of statistical models that allow an analysis of the types of data most commonly gathered by life scientists.

Texte suppl.

A book that should attract curious minds of various backgrounds, knowledge, and expertise in statistics, as well as work nicely to support an enthusiastic teacher of statistical modeling. I thus recommend this book most enthusiastically.

Détails du produit

• Auteurs: Pablo Inchausti, Pablo (Professor of Ecology Inchausti, Inchausti Pablo
• Edition: Oxford University Press
• Contenu: Livre
• Forme du produit: Livre de poche
• Date de parution: 31.10.2022
• Catégorie: Sciences naturelles, médecine, it, technique > Informatique, ordinateurs > Applications, programmes
• EAN: 9780192859020
• ISBN: 978-0-19-285902-0
• Nombre de pages: 480
• Catégories: MATHEMATICS / Probability & Statistics / General, MATHEMATICS / Applied, COMPUTERS / Mathematical & Statistical Software, Probability & statistics, Mathematical & statistical software, Mathematical modelling, Probability and statistics, COMPUTERS / Data Science / Data Visualization, Mathematical and statistical software, COMPUTERS / Database Administration & Management, Data capture and analysis, Data Capture & Analysis