Statistics in MATLAB

Multilevel models and how to fit these models using R, how to employ multilevel modeling with longitudinal data, the valuable graphical options in R, models for categorical dependent variables in both single level and multilevel data. Concludes with Bayesian fitting of multilevel models.

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Fulfilling the need for a practical user's guide, Statistics in MATLAB: A Primer provides an accessible introduction to the latest version of MATLAB and its extensive functionality for statistics. Assuming a basic knowledge of statistics and probability as well as a fundamental understanding of linear algebra concepts, this book covers capabilities in the main MATLAB package and the Statistics Toolbox. The student version of MATLAB presents examples of how MATLAB can be used to analyze data and offers access to a companion website with data sets and additional examples. It contains figures and visual aids to assist in application of the software and explains how to determine what method should be used for analysis. Statistics in MATLAB: A Primer is an ideal reference for undergraduate and graduate students in engineering, mathematics, statistics, economics, biostatistics, and computer science. It is also appropriate for a diverse professional market, making it a valuable addition to the libraries of researchers in statistics, computer science, data mining, machine learning, image analysis, signal processing, and engineering.