Statistics in MATLAB: A Primer: 22 (Chapman & Hall/CRC Computer Science & Data Analysis) - Tapa blanda

Críticas

"The book provides an introductory but comprehensive guide for performing data analysis in MATLAB. It not only covers the most important topics in basic statistics (along with some machine learning techniques), but also touches upon more advanced methods such as kernel density estimation, bootstrap, and principal component analysis...Most of the theories are conveyed in a concise and intuitive way, yet the explanations are quite effective. The implementation of each method in MATLAB is demonstrated using real examples. Detailed MATLAB codes and corresponding numerical and figure outputs are presented with informative MATLAB comments, which makes them easily understood even without the context. The book can be used as a good complementary book to introductory statistics courses...The book can also serve as a perfect guide for self-learners who are not familiar with MATLAB but wish to use MATLAB as a data analysis tool."
―The American Statistician

Reseña del editor

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, the Statistics Toolbox, and the student version of MATLAB
• Presents examples of how MATLAB can be used to analyze data
• Offers access to a companion website with data sets and additional examples
• Contains figures and visual aids to assist in application of the software
• 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.