Analyzing observed or measured data is an important step in applied sciences. The recent increase in computer capacity has resulted in a revolution both in data collection and data analysis. An increasing number of scientists, researchers and students are venturing into statistical data analysis; hence the need for more guidance in this field, which was previously dominated mainly by statisticians.

This handbook fills the gap in the range of textbooks on data analysis. Written in a dictionary format, it will serve as a comprehensive reference book in a rapidly growing field. However, this book is more structured than an ordinary dictionary, where each entry is a separate, self-contained entity. The authors provide not only definitions and short descriptions, but also offer an overview of the different topics. Therefore, the handbook can also be used as a companion to textbooks for undergraduate or graduate courses.

1700 entries are given in alphabetical order grouped into 20 topics and each topic is organized in a hierarchical fashion. Additional specific entries on a topic can be easily found by following the cross-references in a top-down manner. Several figures and tables are provided to enhance the comprehension of the topics and a list of acronyms helps to locate the full terminologies. The bibliography offers suggestions for further reading.

The aim of this volume is to present the basic mathematical aspects of the ranking methods using a didactical approach and to explain their use through examples of relevant applications in different scientific fields. Ranking methods can be applied in several different fileds, such as decision support, toxicology, EU priority lists of toxic chemicals, environmental problems, proteomics and genomics, analytical chemistry, food chemistry and QSAR.
. Covers a wide range of applications, from the environment and toxicology to DNA sequencing
. Incorporates contributions from renowned experts in the field
. Meets the increasing demand for literature concerned with ranking methods and their applications

Molecular Descriptors for Chemoinformatics As every chemist knows, there is a direct (if complex) relationship between the molecular structure of a compound and its chemical behavior. Predicting such behavior is possible by an abstract representation of its structure in terms of chemical similarity parameters, socalled ‘descriptors’. These are most useful in predicting the pharmacological properties of drug candidates, but are also used in predicting reactivity, toxicity and other important chemical characteristics. The number-one reference on the topic now contains a wealth of new data: The entire relevant literature over the past eight years has been pain-stakingly surveyed, resulting in hundreds of new descriptors being added to the list, and some 3,000 new references in the bibliography section. Volume 1 contains an alphabetical listing of around 3300 terms for the chemoinformatic analysis of chemical compound properties, while the second volume contains 6343 references selected from 450 journals with about 7000 authors quoted covering the period from the beginning of molecular descriptor research until the year 2008. In this second edition, several sections have been completely rewritten and organized in a more effective way. The greatly expanded introductory section now contains several “walk-through” reading lists of selected keywords to make the data even more accessible for novice users.