Statistical Analysis of a Control Regression Chart: An Applied Study

Abstract

The use of modern methods and technologies, such as the use of clean fuels, all preserve the environment and reduce air pollution, as well as the risk of disease caused by unclean fuels, which release gases that increase the risk of cancer or respiratory illnesses. For these reasons, we developed the concept for this study by using several significant statistical techniques to identify and address the issue under study. Quality control charts are regarded as one of the most crucial statistical techniques for determining the extent to which the product or service is under control and to measure the extent of acceptance by the consumer. In addition to that, the product obtains the largest profit and the lowest cost. Among the most significant control charts is the control regression chart, which is an effective tool for managing statistical processes. This chart is built by identifying two variables, one of which is linked to each other by a qualitative relationship, such as the response variable (Y) and an explanatory variable (X). The first case, represented by the variable (Y), which represents the per capita share of the gross domestic product, has been taken. This can be equated to purchasing power, and the variable (X), which represents the number of people who depend mainly on clean fuels and modern cooking techniques. Accordingly, we set a control regression chart by determining the upper limit and minimum share if it was discovered that an increase in the per capita share of the domestic product causes an increase in the use of clean fuel in the first case. While the inverse relationship was shown for the second case between the two variables, that is, the number of deaths from domestic air pollution from solid fuels declines with an increase in the number of people who depend mainly on clean fuels and modern cooking techniques, preserving a clean environment reduces the risk of disease as well as environmental pollution.