1. Exploratory Data Analysis
1.1. EDA Introduction

1.1.5.

The Role of Graphics

Quantitative/
Graphical

Statistics and data analysis procedures can broadly be split into two parts:

• quantitative
• graphical

Quantitative

Quantitative techniques are the set of statistical procedures that yield numeric or tabular output. Examples of quantitative techniques include:

• hypothesis testing
• analysis of variance
• point estimates and confidence intervals
• least squares regression

These and similar techniques are all valuable and are mainstream in terms of classical analysis.

Graphical

On the other hand, there is a large collection of statistical tools that we generally refer to as graphical techniques. These include:

• scatter plots
• histograms
• probability plots
• residual plots
• box plots
• block plots

EDA Approach Relies Heavily on Graphical Techniques

The EDA approach relies heavily on these and similar graphical techniques. Graphical procedures are not just tools that we could use in an EDA context, they are tools that we must use. Such graphical tools are the shortest path to gaining insight into a data set in terms of

• testing assumptions
• model selection
• model validation
• estimator selection
• relationship identification
• factor effect determination
• outlier detection

If one is not using statistical graphics, then one is forfeiting insight into one or more aspects of the underlying structure of the data.