Research Guides: Software for Data Analysis: MATLAB

Online Learning -- Software & Coding

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Why MATLAB?

MATLAB is not just a statistical package, but a numerical computing environment that offers much more flexibility than pure statistical softwares, especially for more advanced analysis. It's not a drag-from-menu-bar application, and you need to learn the MATLAB programming language. MATLAB is more used in field of science and engineering, however, the statistics toolbox and econometrics toolbox are very powerful and useful for social science data analysis too.

Where To Find It!

Campus - UCI's Virtual Computing Lab (VCL) and check the Computer Lab Software List

Online Tutorials

Free open tutorials online, ordered by level of proficiency.

MATLAB Summary & Tutorial
Learn the basics of MATLAB programing language from this MATLAB tutorial made by University of Florida.
MATLAB Tutorial by Ed Overman, Dept of Mathematics, Ohio State University
"This tutorial is designed to be a concise introduction to many of the capabilities of MATLAB. It makes no attempt to cover either the range of topics or the depth of detail that you can find in a reference manual, such as MATLAB Guide, 3nd edition by Desmond and Nicholas Higham (which is 500 pages long). This tutorial was initially written to provide students with a free“basic” overview of functions which are useful in an undergraduate course on linear algebra. Over the years it has grown to include courses in ordinary differential equations, mathematical modelling, and numerical analysis. It also includes an introduction to two- and three-dimensional graphics because graphics is often the preferred way to present the results of calculations."
MATLAB Online Documents
The online documents include functions and toolbox guides.

Recommended Books

Applied Statistics by Joaquim P. Marques de Sá
Call Number: Science Library QA276.4 .S13 2007 and eBook
ISBN: 9783540719717
Publication Date: 2007-06-08
Intended for anyone needing to apply statistical analysis to a large variety of science and engineering problems, this book shows how to use SPSS, MATLAB, STATISTICA and R for data description, statistical inference, classification and regression, factor analysis, survival data and directional statistics. The 2nd edition includes the R language, a new section on bootstrap estimation methods and an improved treatment of tree classifiers, plus additional examples and exercises.
A Guide to MATLAB by Brian R. Hunt; Ronald L. Lipsman; Jonathan M. Rosenberg; Kevin R. Coombes; John E. Osborn; Garrett J. Stuck
Call Number: Science Library QA297 .H86 2006
ISBN: 9780521850681
Publication Date: 2006-06-08
This is a short, focused introduction to MATLAB, a comprehensive software system for mathematical and technical computing. It contains concise explanations of essential MATLAB commands, as well as easily understood instructions for using MATLAB's programming features, graphical capabilities, simulation models, and rich desktop interface. Written for MATLAB 7, it can also be used with earlier (and later) versions of MATLAB. This book teaches how to graph functions, solve equations, manipulate images, and much more. It contains explicit instructions for using MATLAB's companion software, Simulink, which allows graphical models to be built for dynamical systems. MATLAB's new "publish" feature is discussed, which allows mathematical computations to be combined with text and graphics, to produce polished, integrated, interactive documents. For the beginner it explains everything needed to start using MATLAB, while experienced users making the switch to MATLAB 7 from an earlier version will also find much useful information here.
Introduction to Spatial Econometrics by Robert Kelley Pace; James LeSage
Call Number: Langson Library HT388 .L47 2009
ISBN: 9781420064247
Publication Date: 2009-01-20
Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not exist. Filling this void, Introduction to Spatial Econometrics presents a variety of regression methods used to analyze spatial data samples that violate the traditional assumption of independence between observations. It explores a wide range of alternative topics, including maximum likelihood and Bayesian estimation, various types of spatial regression specifications, and applied modeling situations involving different circumstances.
Nonparametric Statistics with Applications to Science and Engineering by Kvam, Paul H.
Call Number: Science Library Bar - QA278.8 .V53 2007
ISBN: 0470081473
This book explains most nonparametric statistics methods and its application in MATLAB: probability Basics; Bayesian statistics; goodness of fit; density estimation; curve fitting bootstrap; nonparametric Bayes.