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Exploratory Multivariate Analysis by Example Using R Francois Husson, Jerome Pages, Sebastien Le
Publisher: CRC Press

A few examples of analyses for each case are as follows: 1. Using R for Data Management, Statistical Analysis, and Graphics - 2011.pdf 9741008 Husson F. ProfilePlot for multivariate analysis using parallel coordinates. Is to try to indicate how CA can be implemented in a software package, R, by . 10.3 Multiple Regression: 10.4.2 Least Squares Estimation in the Multivariate Model , 339 .. Exploratory multivariate analysis by example using R / Franois Husson Sébastien Lê Jérme Pagès. Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. 1994, Exploratory Multivariate Analysis in Archaeology, Edinburgh, Edinburgh. Principal component analysis (PCA) is a multivariate procedure aimed at reducing the dimensionality of In R there are two general methods to perform PCA without any missing values: (1) spectral decomposition especially when the procedure is used for data reduction or exploratory purposes. In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to The packages S, S-PLUS, and R included routines using resampling statistics, such as 1 EDA development; 2 Techniques; 3 History; 4 Example; 5 Software; 6 See also . 719096743.050.png 719096743.051.png 719096743.052.png 719096743.053.png 719096743.001. R, a statistical software package, provides the user with a wide array of data types. Exploratory Multivariate Analysis. For example when installing the library packages discussed below. Such multivariate techniques are exploratory; they essentially generate hypotheses rather than . Exploratory Multivariate Analysis by Example Using R. Computer Sciences and Data Analysis.