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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
1979-2010: Contains data similar to that found in the County and City Databook, but on the state and MSA (Metropolitan Statistical Areas) levels.
State and Metropolitan Area Data Book: 2013, First Edition Essential for any economic development official, regional planner, or urban researcher, The State and Metropolitan Area Data Book, previously published by the Census Bureau, is the first edition published by Bernan Press. This valuable resource continues to provide the most complete source of comprehensive and useful information about the nation’s individual states, metropolitan and micropolitan areas, and their component counties. This edition features the latest information on an array of topics such as population, birth and death rates, health coverage, school enrollment, crime rates, income and housing, employment, transportation, and government. Researchers, college students, and data users can easily see the trends and changes affecting the nation today. This edition includes: a complete listing and data for all states, metropolitan areas, including micropolitan areas, and their component counties 2010 census counts and more recent population estimates for all areas results of the 2012 national and state elections expanded vital statistics, communication, and criminal justice data data on migration and commuting habits American Community Survey 1- and 3-year estimates data on health insurance and housing and finance matters accurate and helpful citations to allow the user to directly consult the source source notes and explanations A guide to state statistical abstracts and state information A valuable addition for all academic and public libraries. The State and Metropolitan Area Data Book: 2013 is part of the County and City Extra Series available from Bernan Press. Other books include: County and City Extra: Annual Metro, City, and County Data Book The Who, What, and Where of America: Understanding the American Community Survey Places, Towns, and Townships
A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large.
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.