Kategoria BISAC: Computers >> Mathematical & Statistical Software

The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The...

This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about half-a-dozen major classes of statistical models, and fortified with necessary infrastructure to make the models more fully operable, the framework allows analyses based on many semi-traditional applied statistics models to be performed as a coherent whole.

Since their advent in 1972, GLMs have unified important distributions under a single umbrella with enormous implications. However, GLMs are not flexible enough...

This book on statistical disclosure control presents the theory, applications and software implementation of the traditional approach to (micro)data anonymization, including data perturbation methods, disclosure risk, data utility, information loss and methods for simulating synthetic data. Introducing readers to the R packages sdcMicro and simPop, the book also features numerous examples and exercises with solutions, as well as case studies with real-world data, accompanied by the underlying R code to allow readers to reproduce all results.

The demand for and volume of data from...

Getting data into shape so that the modelling can begin is, as all practitioners know, a time-consuming and difficult process. A Data Scientist's Guide to Acquiring, Cleaning and Managing Data in R addresses what data is and how to manipulate it; acquiring data (web scraping, reading databases, fixed-width, XML), topics in data handling (text/regular expressions, big data, parallel processing; merging, matching, checking for duplicates), and record-keeping (audit trails, getting data back out of R, etc.).

An invaluable source for practitioners who collect and analyse...

This edited volume focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data analytics, time series clustering, integration of data from different sources, as well as social networks. It covers both methodological aspects as well as applications to a wide range of areas including economics, marketing, education, social sciences, medicine, environmental sciences and pharmaceutical industry. In addition, the book's contributions describe basic features of the software...

This book covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating...

Find errors and clean up data easily using SAS

Thoroughly updated, Cody's Data Cleaning Techniques Using SAS, Third Edition, addresses tasks that nearly every data analyst needs to do - that is, make sure that data errors are located and corrected. Written in Ron Cody's signature informal, tutorial style, this book develops and demonstrates data cleaning programs and macros that you can use as written or modify which will make your job of data cleaning easier, faster, and more efficient.

Building on both the author's experience gained from teaching a data...