Monthly Archives: December 2017

Data Science Done Right (the Kitchen Style) #11

Meanings of Covariance Metrics and Principal Component Analysis The Covariance Family metrics (including coefficient of correlation and r squared) may be interpreted in various ways, which indicates that may be poorly understood, hence, and inappropriately used. For example, the usual … Continue reading

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Data Science Done Right (the Kitchen Style) #10

Adding Principal Component Analysis into our cooking mix… If we stop our stepwise regression from the previous chapter on last two dimensions (don’t look at the eigen parameter yet :)), and draw a scatter plot (Fig.10.1): load(url(“http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/diabetes.sav&#8221;)) dim <- c(“glyhb”, … Continue reading

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Data Science Done Right (the Kitchen Style) #9

Picking up loose ends… When previously we were talking about the transformation matrix N-1 – the matrix of changing basis from the original to a basis of the element of the quotient space we project our dataset to – we … Continue reading

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