Sunday January 21st 2018

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When Principal Component Analysis makes sense in business analytics

pcaWhat does that mean from a business analytics point of view? Let us assume that we have a dataset with M parameters (or variables). These could be for example, commodity prices, weekly sales figures, number of hours spent by assembly line workers; in short any business parameter that can have an impact on the performance. The question that PCA helps us to answer fundamentally is this: Which of these M parameters explain a signficant amount of variation contained within the data set? PCA essentially helps to apply an 80-20 rule: can a small subset of parameters (say 20%) explain 80% or more of the variation in the data?

Wiki provides enough mathematical details of how PCA accomplishes this, so we dont need to repeat it here. But we have to point out a couple of key issues to bear in mind while applying PCA. Let us assume that we have sufficient number of samples coming from a historical series or some random experiment so that our data set looks like a M x N matrix, where the M columns are the different parameters, N are the samples and N>M.

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