R And Matlab
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Applied Statistics Using SPSS, STATISTICA and MATLAB
Author: J. P. Marques de Sá
language: en
Publisher: Springer Science & Business Media
Release Date: 2003
Assuming no previous statistics education, this practical reference provides a comprehensive introduction and tutorial on the main statistical analysis topics, demonstrating their solution with the most common software packages. Intended for anyone applying statistical analysis to a wide variety of science and engineering problems, the book shows how to use SPSS, MATLAB, and STATISTICA for data analyses, such as data description, statistical inference, classification and regression, factor analysis, survival data and directional statistics
R and MATLAB
The First Book to Explain How a User of R or MATLAB Can Benefit from the Other In today’s increasingly interdisciplinary world, R and MATLAB® users from different backgrounds must often work together and share code. R and MATLAB® is designed for users who already know R or MATLAB and now need to learn the other platform. The book makes the transition from one platform to the other as quick and painless as possible. Enables R and MATLAB Users to Easily Collaborate and Share Code The author covers essential tasks, such as working with matrices and vectors, writing functions and other programming concepts, graphics, numerical computing, and file input/output. He highlights important differences between the two platforms and explores common mistakes that are easy to make when transitioning from one platform to the other.
An Introduction to MATLAB for Behavioral Researchers
Author: Christopher R. Madan
language: en
Publisher: SAGE Publications
Release Date: 2013-12-18
MATLAB is a powerful data analysis program, but many behavioral science researchers find it too daunting to learn and use. An Introduction to MATLAB for Behavioral Researchers is an easy-to-understand, hands-on guide for behavioral researchers who have no prior programming experience. Written in a conversational and non-intimidating style, the author walks students—step by step—through analyzing real experimental data. Topics covered include the basics of programming, the implementation of simple behavioral analyses, and how to make publication-ready figures. More advanced topics such as pseudo-randomization of trial sequences to meet specified criteria and working with psycholinguistic data are also covered. Interesting behavioral science examples and datasets from published studies, such as visualizing fixation patterns in eye-tracking studies and animal search behavior in two-dimensional space, help develop an intuition for data analysis, which is essential and can only be developed when working with real research problems and real data.