Using Bayes Chart to control the qualitative characteristics of newborns

Abstract

In this study, we will deal with a set of measurable variables (properties) in the quality control charts in this study. There are many qualities whose quality cannot be determined by a single variable, but by a combination of variables that collectively describe the quality of the unit under consideration. Because abnormal children account for a specific number of newly born children, it is necessary to investigate measurement rates for newborn children in order to understand the child's health, which is the foundation of society's health. As a result, the purpose of this study is to create a control chart that uses Bayes statistics, specifically a Bayes chart for qualitatives . We will utilize the values of the final distribution in this chart because it incorporates all of the information accessible during the decision-making process. In this research, a Bayes chart for the (Bnp) number is used to determine the quality of the examined unit. The unit examined in our research represents this newborn child. The research includes how to use the Bayes Theorem to find Bayes estimators for the parameters of the binomial distribution. The study of the control Chart (nP-Chart) uses Bayesian method, including five variables that represent the measurements of newborns, namely (weight, length, head circumference, chest circumference, arm circumference), where these measurements are recorded for (100) cases (child) randomly in Ibn Al-Baladi Hospital in Baghdad for the year (2017) and these observations are divided into (20) samples (set), and the set includes (5) views and these views consist of (52) males , (48) females. We find that predicting the final distribution of any number of observations is the mainstay in building a Bayes chart and making decisions about the progress of the process.