Mathematics, Statistics, and Actuarial Science

If you work with data and need easy access to clear, reliable definitions and explanations of modern statistical and statistics-related concepts, then look no further than this dictionary. Nearly 4000 terms are defined, covering medical, survey, theoretical, and applied statistics, including computational and graphical aspects.

This third edition of a bestseller is now presented as a three-volume set, making it much more accessible and easier to use when searching for information. Maintaining the format that made its predecessors so popular, this edition has been extensively expanded, revised, and updated. It now contains nearly 12,000 entries. Each article provides definitions, formulas, illustrations, web links, bibliographic information, and facts from mathematics, the sciences, and engineering. Written by a single author, the encyclopedia is written in an informal style, making it accessible to anyone who has an interest in mathematics. The three-volume set is beautifully bound with much improved typesetting and format. Dramatically Expanded to Be the Most Comprehensive Library Reference on Mathematics Available Today

To really learn data science, you should not only master the tools--data science libraries, frameworks, modules, and toolkits--but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today's messy glut of data. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability--and how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Sarah J. Greenwald and Jill E. Thomley, eds. Pasadena, CA: Salem Press, 2012. 1191 pp. 3 vols.
Presents some 490 articles showing the math behind our daily lives. Explains how and why math works, and allows readers to better understand how disciplines such as algebra, geometry, calculus, and others affect what we do every day.

* Designed for use by novice computer users, this text begins with the basics, such as starting SPSS, defining variables, and entering and saving data. * All major statistical techniques covered in beginning statistics classes are included: · descriptive statistics · graphing data · prediction and association · parametric inferential statistics · nonparametric inferential statistics · statistics for test construction * Each section starts with a brief description of the statistic that is covered and important underlying assumptions, which help students select appropriate statistics. * Each section describes how to interpret results and express them in a research report after the data are analyzed. For example, students are shown how to phrase the results of a significant and an insignificant t test. * More than 200 screenshots (including sample output) throughout the book show students exactly what to expect as they follow along using SPSS. * A glossary of statistical terms is included, which makes a handy reference for students who need to review the meanings of basic statistical terms. * Practice exercises throughout the book give students stimulus material to use as they practice to achieve mastery of the program. * Thoroughly field-tested; your students are certain to appreciate this book.

This book discusses advanced topics such as R core programing, object oriented R programing, parallel computing with R, and spatial data types. The author leads readers to merge mature and effective methdologies in traditional programing to R programing. It shows how to interface R with C, Java, and other popular programing laguages and platforms.