Statistical process control (SPC) for double-bounded information: Choosing wisely the parametric family for unit data

This article presents a Statistical Process Control (SPC) framework considering the response process as a unit variable, which demands special treatment. This study designed a Shiny app related to data visualization and inferential estimation adopting SPC charts and Extreme Value Theory. We also proposed a new flexible unit probabilistic model (named FlexShape), which is simple yet overcomes skew information and bimodality in historical data, as part of the complex learning task. Results showed that the proposed framework enables it to handle unit data sets. As an example, we presented data storytelling from the water particle monitoring (relative humidity) from one Atacama Desert station, known to be one of the driest areas on Earth, across hidden patterns such as inundation and microweather. Finally, the developed framework makes possible any research on the univariate unit data decision-making, enabling the database import and adjusting some parametric models, and enabling the comparison of different units’ distribution goodness-of-fit.