Generalized linear models (GLM)
19
GLM for binary and proportional responses
Applied Biological Data Analysis
Preface
Statistics in Modern Biology
1
Statistics in biology
Introduction to R
2
Introduction to R
3
R data types and objects
4
Data import and export
5
Data manipulation with R
Exploratory data analysis with R
6
Basic descriptive statistics
7
Summarizing biological data
8
Visualizing biological data
9
Probability distributions for biologists
10
Common problems in biological data
11
Planning your data analysis
Introductory statistics with R
12
Introductory statistics with R
13
Tests comparing groups
14
Tests for continous patterns
15
Tests for contingency
Generalized linear models (GLM)
16
Introduction to generalized linear models (GLM)
17
GLM for continuous response data
18
GLM for count data
19
GLM for binary and proportional responses
Extensions of GLMs: nonlinear and mixed models
20
Nonlinear models for biology
21
Random effects and mixed models
Multivariate data analysis
22
Introduction to multivariate data and data analysis
23
Clustering for biologists
24
Ordination
25
Multivariate hypothesis testing
Alternatives to *p*-values: information-theoretic and Bayesian methods
26
Information theoretic model-based inference
27
Introduction to Bayesian inference for biologists
Bonus material
28
Advanced R graphics
Appendices
A
Source and preparation of NEON data
Generalized linear models (GLM)
19
GLM for binary and proportional responses
19
GLM for binary and proportional responses
Even our curves have curves.
It’s what’s under the curve that counts.
18
GLM for count data
Extensions of GLMs: nonlinear and mixed models