Environmental data analysis : an introduction with examples in R / Carsten Dormann.
Material type:
- text
- unmediated
- volume
- 9783030550226 (pbk.)
- Parametrische Statistik. English
- 23 519.5 DOR 023494
Item type | Current library | Call number | Status | Notes | Date due | Barcode |
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Indian Institute for Human Settlements, Bangalore | 519.5 DOR 023494 (Browse shelf(Opens below)) | Available | Course Reserve | 023494 |
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519.5 BIV 007115 Applied spatial data analysis with R / | 519.5 CRA 022772 The Sage dictionary of statistics : a practical resource for students in the social sciences / | 519.5 DAV 008985 Statistics : | 519.5 DOR 023494 Environmental data analysis : an introduction with examples in R / | 519.5 GAE 004457 Spatial statistics and modeling / | 519.5 GAI 008160 Statistical modeling and inference for social science / | 519.5 GRA 002339 Statistics / |
Includes bibliographical references and index.
Preface-- The technical side: selecting a statistical software--1 Sample statistics--2 Sample statistics in R--3 Distributions, parameters and estimators--4 Distributions, parameters and estimators in R--5 Correlation and association--6 Correlation and association in R--7 Regression Part I--8 Regression in R Part I--9 Regression Part II--10 Regression in R Part II--11 The linear model: t-test and ANOVA--12 The linear model: t-test and ANOVA in R--13 Hypotheses and tests--14 Experimental Design--15 Multiple Regression--16 Multiple Regression in R--17 Outlook--Index
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.-- Provided by publisher.
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