The new statistics with R: an introduction for biologists
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Oxford
Oxford University Press
[2015]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | xi, 199 Seiten Diagramme |
| ISBN: | 9780198729068 9780198729051 |
Internformat
MARC
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| 100 | 1 | |a Hector, Andrew |d 1968- |0 (DE-588)1071469517 |4 aut | |
| 245 | 1 | 0 | |a The new statistics with R |b an introduction for biologists |c Andy Hector |
| 264 | 1 | |a Oxford |b Oxford University Press |c [2015] | |
| 300 | |a xi, 199 Seiten |b Diagramme | ||
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Datensatz im Suchindex
| _version_ | 1804152532787789824 |
|---|---|
| adam_text | Contents
Chapter
1:
introduction
1
1.1
The aim of this book
1
1.2
The
R
programming language for statistics and graphics
2
1.3
Scope
3
1.4
What is not covered
3
1.5
Ihe approach
4
1.6
Ihe new statistics?
5
1.7
Getting started
5
Chapter
2:
Comparing Groups: Analysts of Variance
7
2.1
Introduction
7
2.2
Least squares
12
2.3
Differences between groups
15
2.4
Data in long and wide formats
16
2.5
A first linear model analysis: ANOVA
18
2.6
ANOVA tables
24
2.7
Null hypothesis significance testing
26
2.8
Summary
32
Chapter
3:
Comparing Groups: Student s
ř
test
35
3.1
Introduction
35
3.2
The paired /test
37
3.3
The r-distribution
40
3.4
Confidence intervals
42
3.5
Least significant differences
45
3.6
Assumptions
46
3.7
Summan
48
X
CONTENTS
Chapter
4:
Linear Regression
51
4.1
Introduction
51
4.2
Confidence intervals and prediction intervals
57
4.3
Checking assumptions
60
4.4
Summary
64
Chapters: Comparisons Using Estimates and Intervals
67
5.1
Introduction
67
5.2
Descriptive summaries and statistics: standard deviations
69
5.3
Inferential statistics
71
5.4
Relating different types of interval and error bar
74
5.5
Interpreting confidence intervals
76
5.6
Point estimates and confidence intervals for research synthesis
and meta-analysis
78
5.7
Emphasizing estimates over null hypothesis significance tests
and P-values
80
5.8
Summary
81
Chapter
6:
Interactions
83
6.1
Introduction
83
6.2
Comparing three or more groups
83
6.3
Interactions in factorial, analyses
90
6.4
Summary
99
Chapter
7:
Analysis of Covariance: ANCOVA
101
7.1
Introduction
101
7.2
Panel plots
104
7.3
Interactions in ANCOVA
106
7.4
Summary
112
Chapter
8:
Maximum Likelihood and Generalized Linear Models
113
8.1
Introduction
113
8.2
The
Box
-Сох
power transform
113
8.3
Generalized Linear Models
m R
116
8.4
Summary
120
Chapter
9:
Generalized Linear Models for Data with Non-Normal Distributions
121
9.1
Introduction
121
9.2
Binomial count data
122
CONTENTS
XI
9.3
Binary data
126
9.4
Count data
134
9.5
Summary
138
Chapter
10:
Mixed Effects Models
141
10.1
Introduction
141
10.2
Model building
142
10.3
Mixed-model ANOVA using normal least squares
147
10.4
Maximum likelihood-based mixed-effects models in
R
150
10.5
Likelihood ratio tests of random effects
153
10.6
Random intercepts versus random intercept and slope models
155
10.7
Shrinkage in mixed-effects models
162
10.8
Summary
164
Chapter
11:
Generalized Linear Mixed-Effects Models
165
11.1
Introduction
165
11.2
Model building
167
11.3
GLMMs
m R
171
11.4
Assumptions
176
11.5
Summary
177
Chapter^: Final Thoughts
179
Appendix: A Very Short Introduction to the
R
Programming Language for
Statistics and Graphics
183
A.I Installing
R
183
A.
2
Working with
R
scripts
184
A.3 The
R
language
186
A.
4
Data manipulation: subsetting
190
A.
5
Data entry
191
A.
6
Creating objects inside
R
192
Index
195
Statistical methods are a key tool for all
scientists working with data, but learning the
basic mathematical skills can be one of the most
challenging components of a biologist s training. This
accessible book provides a contemporary introduction
to the classical techniques and modern extensions of linear
model analysis: one of the most useful approaches in the analysis
of scientific data in the life and environmental sciences. It emphasizes an
estimation-based approach that accounts for recent criticisms of the over-use of
probability values, and introduces alternative approaches using information criteria.
Statistics is introduced through worked analyses performed in R, the free open source
programming language for statistics and graphics, which is rapidly becoming the
standard software in many areas of science and technology. These analyses use
real data sets from ecology, evolutionary biology, and environmental science, and
the data sets and
R
scripts are available as support
materia
L
The book s structure and
user friendly style stern from the author s
20
years of experience teaching statistics
to life and environmental scientists at both the undergraduate and graduate levels.
The New Statistics with
R
is suitable for undergraduate and graduate students,
professional researchers, and practitioners in the fields of ecology, evolution,
environmental studies, and computational biology.
|
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| illustrated | Not Illustrated |
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| isbn | 9780198729068 9780198729051 |
| language | English |
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| subject_GND | (DE-588)4729990-3 (DE-588)4705956-4 |
| title | The new statistics with R an introduction for biologists |
| title_auth | The new statistics with R an introduction for biologists |
| title_exact_search | The new statistics with R an introduction for biologists |
| title_full | The new statistics with R an introduction for biologists Andy Hector |
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| title_short | The new statistics with R |
| title_sort | the new statistics with r an introduction for biologists |
| title_sub | an introduction for biologists |
| topic | Biostatistik (DE-588)4729990-3 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Biostatistik R Programm |
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