Four years have passed since the first edition of this book. During this time I have had the opportunity to apply it in classes obtaining feedback from students and inspiration for improvements. I have also benefited from many comments by users of the book. For the present second edition large parts of the book have undergone major revision, although the basic concept – concise but sufficiently rigorous mathematical treatment with emphasis on computer applications to real datasets –, has been retained. The second edition improvements are as follows: • Inclusion of R as an application tool. As a matter of fact, R is a free software product which has nowadays reached a high level of maturity and is being increasingly used by many people as a statistical analysis tool. • Chapter 3 has an added section on bootstrap estimation methods, which have gained a large popularity in practical applications. • A revised explanation and treatment of tree classifiers in Chapter 6 with the inclusion of the QUEST approach. • Several improvements of Chapter 7 (regression), namely: details concerning the meaning and computation of multiple and partial correlation coefficients, with examples; a more thorough treatment and exemplification of the ridge regression topic; more attention dedicated to model evaluation. • Inclusion in the book CD of additional MATLAB functions as well as a set of R functions. • Extra examples and exercises have been added in several chapters. • The bibliography has been revised and new references added.
Cuprins
Presenting and Summarising the Data.- Estimating Data Parameters.- Parametric Tests of Hypotheses.- Non-Parametric Tests of Hypotheses.- Statistical Classification.- Data Regression.- Data Structure Analysis.- Survival Analysis.- Directional Data.
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