Foundations
NIST/SEMATECH Sections 1.1 – 1.2
Core concepts of Exploratory Data Analysis — what EDA is, how it differs from classical and Bayesian approaches, the role of graphics, underlying assumptions, and the essential 4-plot diagnostic.
What is EDA?
Section 1.1
Learn what Exploratory Data Analysis is, how it differs from classical and Bayesian approaches, and why EDA is essential for understanding data before formal modeling
The Role of Graphics in EDA
Section 1.1.5
Discover why graphical methods are central to Exploratory Data Analysis and how visual techniques reveal patterns that summary statistics can miss
EDA Problem Categories
Section 1.1.7
The eight general problem categories in EDA: univariate, control, comparative, screening, optimization, regression, time series, and multivariate — each with its data structure, model, output, and recommended techniques
Underlying Assumptions
Section 1.2
Understand the four underlying assumptions of statistical analysis — random drawings, fixed distribution, fixed location, and fixed variation — and why testing them matters
When Assumptions Fail
Section 1.2.5
Understand the consequences when underlying statistical assumptions are violated and how non-compliance affects the validity of analysis results
The 4-Plot for Assumption Testing
Section 1.3.3.32
Learn how the 4-plot technique tests all four underlying assumptions simultaneously using a run sequence plot, lag plot, histogram, and normal probability plot