Exploratory data analysis (EDA) is an approach to data analysis that is mainly based on the use of graphical tools- The use of data analysis helps the
researcher/analyst to meet a few objectives:

Maximise the insight into the data set-

Uncover the underlying data structure-

Extract the most important variables-

Identify outliers and anomalies-

Test underlying assumptions-

Develop parsimonious models-

Determine optimal factor settings-

The main reasons for using intensively graphical analysis is to help the researcher to approach his/her analysis in an open-minded way, where graphics will
help to uncover unsuspected patterns in the data set- EDA is often supported by a combination of different techniques like:

plotting the raw data using data traces, histograms, bihistograms, probability plots, lag plots, block plots and Youden plots;

plotting simple statistics such as mean plots, standard deviation plots, box plots and main effects plots of the raw data;

positioning plots to maximise the recognition of patterns by using multiple plots-

After the data collection process is finished, there are a few questions that need to be considered by the researcher/analyst as slhe starts to understand the
basic characteristics of his/her data set- Some of the main questions to be considered are:

How do you do an exploratory data analysis?

What kind of techniques should be used to support your analysis?

How many techniques should be used?

What is the main focus of the selected techniques?

What are the main benest of using graphical analysis?