logo

EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link:  https://ebookbell.com/faq 


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Environmental Data Analysis An Introduction With Examples In R Carsten Dormann

  • SKU: BELL-22122350
Environmental Data Analysis An Introduction With Examples In R Carsten Dormann
$ 31.00 $ 45.00 (-31%)

4.4

82 reviews

Environmental Data Analysis An Introduction With Examples In R Carsten Dormann instant download after payment.

Publisher: Springer
File Extension: PDF
File size: 9 MB
Pages: 351
Author: Carsten Dormann
ISBN: 9783030550196, 3030550192
Language: English
Year: 2020

Product desciption

Environmental Data Analysis An Introduction With Examples In R Carsten Dormann by Carsten Dormann 9783030550196, 3030550192 instant download after payment.

Environmental Data Analysis is an introductory statistics textbook for environmental science. It covers descriptive, inferential and predictive statistics, centred on the Generalized Linear Model. The key idea behind this book is to approach statistical analyses from the perspective of maximum likelihood, essentially treating most analyses as (multiple) regression problems. The reader will be introduced to statistical distributions early on, and will learn to deploy models suitable for the data at hand, which in environmental science are often not normally distributed. To make the initially steep learning curve more manageable, each statistical chapter is followed by a walk-through in a corresponding R-based how-to chapter, which reviews the theory and applies it to environmental data. In this way, a coherent and expandable foundation in parametric statistics is laid, which can be expanded in advanced courses.The content has been “field-tested” in several years of courses on statistics for Environmental Science, Geography and Forestry taught at the University of Freiburg.

Related Products