Multivariate Data Analysis With Matlab
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Automating Vibrational Spectroscopy Data Preprocessing and Multivariate Analysis with MATLAB(R)
This Spotlight teaches the commands necessary to analyze spectroscopic data (Raman/FTIR) using MATLAB. It explains how to build an analysis routine step by step and perform pre-processing and multivariate analysis with a single click. The script for support vector machines (SVMs) is also briefly addressed so that readers can build a script tailored to their own laboratory routine.
Handbook of Data Visualization
Author: Chun-houh Chen
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-12-18
Visualizing the data is an essential part of any data analysis. Modern computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data displays. This book gives an overview of modern data visualization methods, both in theory and practice. It details modern graphical tools such as mosaic plots, parallel coordinate plots, and linked views. Coverage also examines graphical methodology for particular areas of statistics, for example Bayesian analysis, genomic data and cluster analysis, as well software for graphics.
MATLAB® Recipes for Earth Sciences
Author: Martin Trauth
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-10-13
MATLAB® is used in a wide range of applications in geosciences, such as image processing in remote sensing, generation and processing of digital elevation models and the analysis of time series. This book introduces methods of data analysis in geosciences using MATLAB such as basic statistics for univariate, bivariate and multivariate datasets, jackknife and bootstrap resampling schemes, processing of digital elevation models, gridding and contouring, geostatistics and kriging, processing and georeferencing of satellite images, digitizing from the screen, linear and nonlinear time-series analysis and the application of linear time-invariant and adaptive filters. The revised and updated Second Edition includes new subchapters on windowed Blackman-Tukey, Lomb-Scargle and Wavelet powerspectral analysis, statistical analysis of point distributions and digital elevation models, and a full new chapter on the statistical analysis of directional data. The text includes a brief description of each method and numerous examples demonstrating how MATLAB can be used on data sets from earth sciences. All MATLAB recipes can be easily modified in order to analyse the reader's own data sets.