This is a book about EDA today.
1
Preface
2
What is exploratory data analysis?
2.1
Current interpretations
2.2
Differentiating from traditional analysis
2.2.1
From the case study
2.2.2
EDA approach
2.2.3
Summarising the difference
2.2.4
Reality check
2.2.5
What is EDA?
2.2.6
Isn’t it data snooping?
2.2.7
Why aren’t there more resources on EDA?
2.3
The origins of EDA
2.3.1
Guiding principles
2.3.2
Scratching down numbers
2.3.3
Schematic summary
2.3.4
Easy re-expression
2.3.5
Effective comparison
2.3.6
Plots of relationship
2.4
Exercises
3
Initial Data Analysis
3.1
Data description
3.1.1
Checking the data type
3.1.2
Checking the data quality
3.1.3
Check on data collection method
3.1.4
Check for experimental data
3.2
Model formulation
3.2.1
Review: Linear models in R
3.3
Model formulation
3.3.1
Experimental Design
3.4
Summary
4
Working with a single variable
4.1
Possible features of continuous variables
4.2
Numerical features of a single contiuous variables
4.3
Outliers
4.4
Closer look at the
boxplot
4.5
Robust statistics: measure of central tendency
4.6
Robust statistics: measure of dispersion
4.7
Transformations
5
Bivariate dependencies and relationships
6
Going beyond two variables
7
Making comparisons between groups and strata
8
Sculpting data using models
9
Exploring data having a space and time context
10
Is what you see really there?
11
Final words
References
Published with bookdown
Exploratory Data Analysis in the 21st Century
Chapter 9
Exploring data having a space and time context