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Linear models for longitudinal experiments
Statistics for the Experimental Bench Biologist
Front Matter
Preface
1
Analyzing experimental data with a linear model
2
Getting Started – R Projects and R Markdown
3
Data – Reading, Wrangling, and Writing
4
Plotting Models
5
Variability and Uncertainty (Standard Deviations, Standard Errors, and Confidence Intervals)
6
P-values
7
Errors in inference
8
An introduction to linear models
9
Linear models with a single, continuous
X
(“linear regression”)
10
Linear models with a single, categorical
X
(“t-tests” and “ANOVA”)
11
Model checking
12
Multiple tests – why and when to adjust
p
-values
13
Issues in inference
14
Linear models with two categorical
\(X\)
– Factorial linear models (“two-way ANOVA”)
15
Linear models with added covariates (“ANCOVA”)
16
Models for non-independence – linear mixed models
17
Models for counts, binary responses, skewed responses, and proportions – Generalized Linear Models
18
Linear models for longitudinal experiments
18
Linear models for longitudinal experiments
17
Models for counts, binary responses, skewed responses, and proportions – Generalized Linear Models