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This primer provides an accessible introduction to MATLAB version 8 and its extensive functionality for statistics. Fulfilling the need for a practical user's guide, the book covers capabilities in the main MATLAB package, the Statistics Toolbox, and the student version of MATLAB, presenting examples of how MATLAB can be used to analyze data. It explains how to determine what method should be used for analysis, and includes figures, visual aids, and access to a companion website with data sets and additional examples.
MATLAB is an interactive system for numerical computation that is widely used for teaching and research in industry and academia. It provides a modern programming language and problem solving environment, with powerful data structures, customizable graphics, and easy-to-use editing and debugging tools. This third edition of MATLAB Guide completely revises and updates the best-selling second edition and is more than 30 percent longer. The book remains a lively, concise introduction to the most popular and important features of MATLAB and the Symbolic Math Toolbox. Key features are a tutorial in Chapter 1 that gives a hands-on overview of MATLAB; a thorough treatment of MATLAB mathematics, including the linear algebra and numerical analysis functions and the differential equation solvers; and a web page at http://www.siam.org/books/ot150 that provides example program files, updates, and links to MATLAB resources. The new edition contains color figures throughout; includes pithy discussions of related topics in new ?Asides" boxes that augment the text; has new chapters on the Parallel Computing Toolbox, object-oriented programming, graphs, and large data sets; covers important new MATLAB data types such as categorical arrays, string arrays, tall arrays, tables, and timetables; contains more on MATLAB workflow, including the Live Editor and unit tests; and fully reflects major updates to the MATLAB graphics system. This book is suitable for both beginners and more experienced users, including students, researchers, and practitioners.
This is a simple book on Statistics using MATLAB . There is a review of MATLAB in the first few chapters followed by four chapters on Statistics. This topic is very important for students and researchers in fields such as biological sciences, behavioral sciences, psychological sciences, marine science, etc. Two "statistics" chapters cover the basics of measures of central tendency, measures of dispersion, and graphical means of statistical output. There is no coverage of probability theory - only basic statistical concepts. The last two chapters cover the important topic of regression analysis in some detail. I chose this topic because regression analysis is the main statistical tool used in building models. Some readers even wanted me to include topics like hypothesis testing and ANOVA, but I feel that these topics should not be covered in a beginner's book. These topics can be found fully illustrated in specialized MATLAB books on statistics - check the updated list of references for titles of three to four books in this regard. All four chapters on statistics employ the various "statistics" commands found in the main MATLAB package, without resort to the specialized Statistics Toolbox . It should also be noted that the Statistics Toolbox is purchased separately from the MATLAB package and consists of a set of advanced MATLAB commands for specialized and advanced statistical tools, and these are beyond the scope of this book. Numerous other statistics toolboxes are also available on the market.
This book on Statistics using MATLAB is a straightforward guide. It comprises a MATLAB review in the initial chapters, followed by four chapters dedicated to Statistics. The subject matter is essential for students and researchers alike, especially those in fields such as marine science, biological sciences, psychological sciences, behavioral sciences, among others. The "statistics" chapters cover fundamental statistical concepts, such as measures of central tendency, measures of dispersion, and graphical statistical output. Probability theory is not addressed in this beginner's book. The last two chapters delve into regression analysis, which is the main statistical tool used in building models. Though some readers suggested covering topics like hypothesis testing and ANOVA, the author felt they were beyond the scope of this beginner's book. However, these topics are thoroughly illustrated in specialized MATLAB books on statistics, and readers can refer to the updated reference list for titles of recommended books. The four chapters on statistics use the various "statistics" commands in the primary MATLAB package without resorting to the specialized Statistics Toolbox. The Statistics Toolbox is separately purchased from the MATLAB package and provides a set of advanced MATLAB commands for specialized and advanced statistical tools, which are beyond the book's scope. Numerous other statistics toolboxes are also available in the market.
As with the bestselling first edition, Computational Statistics Handbook with MATLAB, Second Edition covers some of the most commonly used contemporary techniques in computational statistics. With a strong, practical focus on implementing the methods, the authors include algorithmic descriptions of the procedures as well as
The Elements of MATLAB Style is a guide for both new and experienced MATLAB programmers. It provides a comprehensive collection of standards and guidelines for creating solid MATLAB code that will be easy to understand, enhance, and maintain. It is written for both individuals and those working in teams in which consistency is critical. This is the only book devoted to MATLAB style and best programming practices, focusing on how MATLAB code can be written in order to maximize its effectiveness. Just as Strunk and White's The Elements of Style provides rules for writing in the English language, this book provides conventions for formatting, naming, documentation, programming and testing. It includes many concise examples of correct and incorrect usage, as well as coverage of the latest language features. The author also provides recommendations on use of the integrated development environment features that help produce better, more consistent software.
A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data. This practical, hands-on approach is unique among data analysis textbooks and guides, and equips the reader with the tools necessary for real-world neural data analysis. The book begins with an introduction to MATLAB, the most common programming platform in neuroscience, which is used in the book. (Readers familiar with MATLAB can skip this chapter and might decide to focus on data type or method type.) The book goes on to cover neural field data and spike train data, spectral analysis, generalized linear models, coherence, and cross-frequency coupling. Each chapter offers a stand-alone case study that can be used separately as part of a targeted investigation. The book includes some mathematical discussion but does not focus on mathematical or statistical theory, emphasizing the practical instead. References are included for readers who want to explore the theoretical more deeply. The data and accompanying MATLAB code are freely available on the authors' website. The book can be used for upper-level undergraduate or graduate courses or as a professional reference. A version of this textbook with all of the examples in Python is available on the MIT Press website.