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Zusatztext "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 Informationen zum Autor Wendy L. Martinez is a mathematical statistician with the Bureau of Labor Statistics in Washington, District of Columbia, USA. She has co-authored two additional successful Chapman Hall/CRC books on MATLAB and statistics, and has been using MATLAB for more than 15 years to solve problems and conduct research in statistics and engineering. MoonJung Cho is a mathematical statistician with the Bureau of Labor Statistics in Washington, District of Columbia, USA. She has more than10 years of experience in survey methodology research and applications, and is knowledgeable of other software packages, such as SAS and R. She is able to use this knowledge to enhance the utility of this book to users of other statistical software packages. Zusammenfassung 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. Inhaltsverzeichnis MATLAB Basics. Visualizing Data. Descriptive Statistics. Probability Distributions. Hypothesis Testing. Model–Building with Regression Analysis. Multivariate Analysis. Classification and Clustering. ...
