  We reformulate and reframe a series of increasingly complex parametric
statistical topics into a framework of response-vs-covariate (Re-Co) dynamics
that is described without any explicit functional structures. Then we resolve
these topics' data analysis tasks by discovering major factors underlying such
Re-Co dynamics by only making use of data's categorical nature. The major
factor selection protocol at the heart of Categorical Exploratory Data Analysis
(CEDA) paradigm is illustrated and carried out by employing Shannon's
conditional entropy (CE) and mutual information ($I[Re; Co] $) as two key
Information Theoretical measurements. Through the process of evaluating these
two entropy-based measurements and resolving statistical tasks, we acquire
several computational guidelines for carrying out the major factor selection
protocol in a do-and-learn fashion. Specifically, practical guidelines are
established for evaluating CE and $I[Re; Co] $ in accord with the criterion
called [C1:confirmable]. Via [C1:confirmable] criterion, we make no attempts on
acquiring consistent estimations of these theoretical information measurements.
All evaluations are carried out on a contingency table platform, upon which the
practical guidelines also provide ways of lessening effects of curse of
dimensionality. We explicitly carry out six examples of Re-Co dynamics, within
each of which, several widely extended scenarios are also explored and
discussed.
