A Minimal Book Example
Who is this book for?
I Questions about your goals
1
What is your goal?
1.1
Exploratory or hypothesis generation
1.2
Inferential or hypothesis testing “Are things different”
1.3
Physical or mechanistic predictions - you can only statistics them away sometimes
2
Types of resources
References
3
Distributions
3.1
Bounded
3.2
Heteroscedascitity vs homoscedasicity
3.3
Theoretical, existing, known
3.4
Simulated, randomized, computational
3.5
When to use either?
II Specific tests
How to use this section
Principal components analysis
3.6
Explanation.
3.6.1
Questions and data types
3.6.2
Key assumptions
3.6.3
Key distinctions among methods within PCA
3.7
email text
3.7.1
CART/ctree explanations
3.7.2
Examples of PCA in the wild:
3.7.3
Once you have decided to use it, check implementation
Intraclass Correlation Coefficient a.k.a. repeatability
3.8
Explanation
3.8.1
The basics
3.8.2
More technical
3.8.3
Most technical
3.9
Examples “in the wild”
Supervised learning
3.10
Decision trees (also known as classification and regression trees [CART], conditional inference trees)
3.10.1
General explanations and interpretation
3.10.2
Examples of CART in the wild
3.10.3
How and why to run the
ctree
version of CART/decision trees
What each section has
3.11
Explanation
3.11.1
The basics
3.11.2
More technical
3.11.3
Most technical
3.12
Examples “in the wild”
Published with bookdown
Statistics for Scared People
Chapter 2
Types of resources
Peer-reviewed vs not: what can you cite? What helps?