Quality Decisions Based on Time between Events Data Analysis

Abstract

Good decisions (Quality Decisions) depend on scientific analysis of data. Data are collected, generally, in two ways: 1) one sample of suitable size, 2) subsequent samples, at regular intervals of time. Often the data are considered normally distributed. This is wrong because the data must be analysed according to their distribution: Decisions are different. In several cases the data are exponentially distributed: we see how to scientifically deal with Control Charts (CC) to decide; this is opposite to what gives the T Charts that are claimed to be a good method for dealing with “rare events”: The Minitab Software (19 & 20 & 21) for “T Charts” is considered. The author will compare some methods, found in the literature with the author’s Theory RIT (Reliability Integral Theory): We will see various cases found in the literature. Classical Shewhart Control Charts and the TBE (Time Between Events) Control Charts have been considered: it appears that with RIT the future decisions will be both sounder and cheaper, for data is exponentially distributed. The novelty of the paper is in the scientific way of dealing with the Control Charts and their Control Limits, both with normally distributed data and with exponentially distributed data. In this way, a lot of wrong published papers on “Time Between Events” are to be discarded, even if their authors claim “We used Standard Statistical methods, typical in the vast literature of similar papers”. The author had to self-cite because it seems the only one that has been fighting for years for “Papers Quality”; he humbly asked the readers to inform him if some people did the same