Exploratory Data Analysis with MATLAB, Second Edition - download pdf or read online

By Wendy L. Martinez, Angel R. Martinez, Angel Martinez, Jeffrey Solka

ISBN-10: 1439812209

ISBN-13: 9781439812204

Because the book of the bestselling first version, many advances were made in exploratory facts research (EDA). overlaying leading edge ways for dimensionality aid, clustering, and visualization, Exploratory info research with MATLAB®, moment version makes use of a number of examples and functions to teach how the equipment are utilized in perform. New to the second one version Discussions of nonnegative matrix factorization, linear discriminant research, curvilinear part research, self sustaining part research, and smoothing splines An accelerated set of tools for estimating the intrinsic dimensionality of an information set a number of clustering tools, together with probabilistic latent semantic research and spectral-based clustering extra visualization equipment, equivalent to a rangefinder boxplot, scatterplots with marginal histograms, biplots, and a brand new procedure referred to as Andrews’ photos directions on a unfastened MATLAB GUI toolbox for EDA Like its predecessor, this variation maintains to target utilizing EDA tools, instead of theoretical elements. The MATLAB codes for the examples, EDA toolboxes, info units, and colour types of all figures can be found for obtain at http://pi-sigma.info

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Additional resources for Exploratory Data Analysis with MATLAB, Second Edition (Chapman & Hall CRC Computer Science & Data Analysis)

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This yields 12 weight measurements, and each corresponds to a class of particle size. 0mm (what is in the largest sieve). Flenley and Olbricht [1993] consider the classes as outlined above, and they apply various multivariate and exploratory data analysis techniques such as principal component analysis and projection pursuit. html. book Page 19 Thursday, November 11, 2010 8:51 AM Introduction to Exploratory Data Analysis 19 More information on the original data set can be found at this website.

We discuss several 1-D standardization methods below. , on the column of X) separately. Transformation Using the Standard Deviation The first standardization we discuss is called the sample z-score, and it should be familiar to most readers who have taken an introductory statistics class. book Page 23 Thursday, November 11, 2010 8:51 AM Introduction to Exploratory Data Analysis 23 where x is the original observed data value, x is the sample mean, and s is the sample standard deviation. In this standardization, the new variate z will have a mean of zero and a variance of one.

As we stated in the beginning of this chapter, the seminal book on EDA is Tukey [1977], but the text does not include the more up-to-date view based on current computational resources and methodology. Similarly, the short book on EDA by Hartwig and Dearing [1979] is an excellent introduction to the topic and a quick read, but it is somewhat dated. For the graphical approach, the reader is referred to du Toit, Steyn, and Stumpf [1986], where the authors use SAS to illustrate the ideas. They include other EDA methods such as multidimensional scaling and cluster analysis.

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Exploratory Data Analysis with MATLAB, Second Edition (Chapman & Hall CRC Computer Science & Data Analysis) by Wendy L. Martinez, Angel R. Martinez, Angel Martinez, Jeffrey Solka


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