Pedagogy of transformation of a random variable, censoring, and truncation of data

Abstract

AbstractThe author has been incorporating data analytics into the probability course for students pursuing baccalaureate programs in engineering through weekly assignments and end‐of‐the‐term projects. Data analytics makes the probability course much more relevant to the current trends in business, industry, and medicine that rely on data. The discussion related to the coronavirus pandemic about missing and incomplete data, the inability to pinpoint the exact start of the disease, and so forth offered an opportunity to expand activities in data analytics by examining “truncation” and “censoring” of data. In simple terms, truncation implies the exclusion of data (either not collected or ignored) in a certain range. Censoring implies redefining the data in a certain range while keeping the rest of the original data. A demo was created to explore the association of the transformation of random variables (mixed and conditional) to censoring and truncation. The demo utilized the theory of random variables, simulation, and computational techniques to examine and establish the link between the mixed transformation of variables and censoring. A similar link of conditional transformation of variables to truncation was also seen. The demo was used as the basis for a weekly assignment to the students. The analysis and results suggest that it is possible to expand the scope of the course in engineering probability to provide training in contemporary topics in data analytics.