Statistics

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bookdown: Authoring Books and Technical Documents with R Markdown

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Yihui Xie
December 19, 2016

bookdown: Authoring Books and Technical Documents with R Markdown presents a much easier way to write books and technical publications than traditional tools such as LaTeX and Word. The bookdown package inherits the simplicity of syntax and flexibility for data analysis from R Markdown, and extends ...

Modern Data Science with R

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Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton
February 2, 2017

Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programmi...

Applied Time Series Analysis with R, Second Edition

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Wayne A. Woodward, Henry L. Gray, Alan C. Elliott
December 20, 2016

Virtually any random process developing chronologically can be viewed as a time series. In economics closing prices of stocks, the cost of money, the jobless rate, and retail sales are just a few examples of many. Developed from course notes and extensively classroom-tested, Applied Time Series Anal...

Testing R Code

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Richard Cotton
January 19, 2017

Learn how to write R code with fewer bugs. The problem with programming is that you are always one typo away from writing something silly. Likewise with data analysis, a small mistake in your model can lead to a big mistake in your results. Combining the two disciplines means that it is all too easy...

Change-Point Analysis in Nonstationary Stochastic Models

Boris Brodsky
March 23, 2017

This book covers the development of methods for detection and estimation of changes in complex systems. These systems are generally described by nonstationary stochastic models, which comprise both static and dynamic regimes, linear and nonlinear dynamics, and constant and time-variant structures...

Continuous Improvement, Probability, and Statistics: Using Creative Hands-On Techniques

William Hooper
March 23, 2017

This book is for those that have struggled with the concept of statistics, have a genuine fear of data, and think the world of continuous improvement and experimentation is designed for few to understand. This book answers the question, why can’t every operator, technician, student, manager and...

Equivalence: Elizabeth L. Scott at Berkeley

Amanda L. Golbeck
March 22, 2017

Equivalence: Elizabeth L. Scott at Berkeley is the compelling story of one pioneering statistician’s relentless twenty-year effort to promote the status of women in academe and science. Part biography and part microhistory, the book provides the context and background to understand Scott’s...

Meta-Analysis in Psychiatry Research: Fundamental and Advanced Methods

Mallikarjun B. Hanji
March 21, 2017

This book introduces the latest meta-analytical methods and discusses their applications in the field of psychiatry. A comprehensive list of methods used in meta-analysis has been described in simple language and demonstrated with real-time examples. This informative volume explains the importance...

Improving Population Health Using Electronic Health Records: Methods for Data Management and Epidemiological Analysis

Neal D. Goldstein
March 16, 2017

Electronic health records (EHRs) have become commonplace in the medical profession. Health data are readily captured and permanently stored in a digital fashion, and consequently, are increasingly being utilized in health research. The quality of this research depends upon the investigator’s...

The R Primer, Second Edition

Claus Thorn Ekstrom
March 01, 2017

Praise for the first edition: "This book provides a good introduction to R, using a clear layout and detailed, reproducible examples. An ideal tool for any new R user. … A wide range of topics are covered, making the book suitable for a variety of readers, from undergraduate students to...

Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials

Toshiro Tango
February 16, 2017

Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials is the first book focused on the application of generalized linear mixed models and its related models in the statistical design and analysis of repeated measures from randomized controlled trials. The...

Methods in Comparative Effectiveness Research

Constantine Gatsonis, Sally C. Morton
February 07, 2017

Comparative effectiveness research (CER) is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care (IOM 2009). CER is conducted to develop evidence that...

Modern Data Science with R

Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton
February 02, 2017

Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical...

Animal Movement: Statistical Models for Telemetry Data

Mevin B. Hooten, Devin S. Johnson, Brett T. McClintock, Juan M. Morales
January 26, 2017

The study of animal movement has always been a key element in ecological science, because it is inherently linked to critical processes that scale from individuals to populations and communities to ecosystems. Rapid improvements in biotelemetry data collection and processing technology have given...

Data Mining with R: Learning with Case Studies, Second Edition

Luis Torgo
January 19, 2017

Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material,...

Testing R Code

Richard Cotton
January 19, 2017

Learn how to write R code with fewer bugs. The problem with programming is that you are always one typo away from writing something silly. Likewise with data analysis, a small mistake in your model can lead to a big mistake in your results. Combining the two disciplines means that it is all too...

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