Unraveling water monitoring association towards weather attributes for response proportions data: A unit-Lindley learning.

ABSTRACT: Learning techniques involve unraveling regression structures, which aim to analyze in a probabilistic frame the associations across variables of interest. Thus, analyzing fraction and/or proportion data may not be adequate with standard regression procedures, since the linear regression models generally assume that the dependent (outcome) variable is normally distributed. In this manner, we propose a statistical model called unit-Lindley regression model, for the purpose of Statistical Process Control (SPC). As a result, a new control chart tool was proposed, which targets the water monitoring dynamic, as well as the monitoring of relative humidity, per minute, of Copiapó city, located in Atacama Desert (one of the driest non-polar places on Earth), north of Chile. Our results show that va

SUBMITTER: Ferreira PH 

PROVIDER: S-EPMC9565758 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature