Government Authorities Have Seized GM's Plant In Venezuela
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What's more powerful: government or industry? We may be about to find out as General Motors is headed for a battle with the government of Venezuela. Read more »
Principal Component Analysis (PCA) is a dimension reduction technique. We obtain a set of factors which summarize, as well as possible, the information available in the data. The factors are linear combinations of the original variables. The approach can handle only quantitative variables. We have presented the PCA in previous tutorials. In this paper, we describe in details two indicators used for the checking of the interest of the implementation of the PCA on a dataset: the Bartlett's sphericity test and the KMO index. They are directly available in some commercial tools (e.g. SAS or SPSS). Here, we describe the formulas and we show how to program them under R. We compare the obtained results with those of SAS on a dataset. Keywords : principal component analysis, pca, spss, sas, proc factor, princomp, kmo index, msa, measure of sampling adequacy, bartlett's sphericity test, xlsx package, psych package, R software Components : VARHCA, PRINCIPAL COMPONENT ANALYSIS Tutorial : ...
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The linear discriminant analysis has been enhanced. All operations are performed in a single pass on the data. A multithreaded version of the linear discriminant analysis has been added. It improves the execution speed by distributing calculations on any hearts (computer with a multi-core processor) or processors (multi-processor computer) available on the computer. A tutorial will describe the behavior of these new implementations on some large databases. Keywords : linear discriminant analysis, multihreaded implementation Sipina website : Sipina Download : Setup file
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