The centralized empirical cumulative average deviation function is utilized to develop
both Ada-plot and Uda-plot as alternatives to Ad-plot and Ud-plot introduced by the author.
Analogous to Ad-plot, Ada-plot can identify symmetry, skewness, and outliers of the data
distribution. The Uda-plot is as exceptional as Ud-plot in assessing normality. The d-value
that quantifies the degree of proximity between the Uda-plot and the graph of the estimated
normal density function helps guide to make decisions on confirmation of normality. Extreme
values in the data can be eliminated using the 1.5IQR rule to create its robust version if user
demands. Full description of the methodology can be found in the article by Wijesuriya (2025a)