Can Randomness be an Element in Multiple Regression

Richard Boire
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Richard Boire: Boire Filler Group, Canada

Current Trends in Biomedical Engineering & Biosciences, 2016, vol. 1, issue 1, 1-2

Abstract: In some of the more recent literature and discussions, discussion has ensued about the use of pure random or noise variables that end up as key regression variables. In our big data environment with millions of records and thousands of variables, intuitively one might think that random or spurious variables might be a normal outcome in many models. As a data miner, I am always intrigued by fellow colleagues who arrive at certain findings based on their research and work. When considering the validity of these comments, I harken back to my experience in building hundreds of models over the years in a variety of different industry sectors. Practitioners typically adopt techniques that ensure the unlikelihood of these variables in any final regression equation. These techniques will be discussed later on in this article.

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Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jctbeb:v:1:y:2016:i:1:p:1-2

DOI: 10.19080/CTBEB.2016.01.555551

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