Data Analysis and Visualization

A concise, introductory text that covers biostatistical principles and focuses on the common types of data encountered in public health and biomedical fields. Older edition available in the HHSL Book Stacks.

This practical guide that exposes students to statistical tests they are likely to encounter in the health sciences, with careful conceptual explanations and a minimum of algebra. Older editions available on reserve at the HHSL Library Service Desk, and in the Books Stacks.

This book is provides a thorough introduction to statistics for students and professors in the biological, life, and health sciences.

Practical advice on key topics in statistics, including: research plan creation; data collection; hypothesis formulation and testing; and p-value interpretations.

This book leads you through the methods, techniques, and computations of statistics necessary for success in the medical field. Older edition available in the HHSL Book Stacks.

Authored by a Tufts biology professor, this is a handbook of measurements, calculations and quantitative skills for use at the bench. The book describes various mathematical principles, basic statistics, and tasks involving numbers, such as calibrating lab equipment and making solutions.

This edition is a reprint of the second edition published in 2000 by Brooks/Cole and then Cengage Learning. Principles of Biostatistics is aimed at students in the biological and health sciences who wish to learn modern research methods. It is based on a required course offered at the Harvard School of Public Health. In addition to these graduate students, many health professionals from the Harvard medical area attend as well. The book is divided into three parts. The first five chapters deal with collections of numbers and ways in which to summarize, explore, and explain them. The next two chapters focus on probability and introduce the tools needed for the subsequent investigation of uncertainty. It is only in the eighth chapter and thereafter that the authors distinguish between populations and samples and begin to investigate the inherent variability introduced by sampling, thus progressing to inference. Postponing the slightly more difficult concepts until a solid foundation has been established makes it easier for the reader to comprehend them. All supplements, including a manual for students with solutions for odd-numbered exercises, a manual for instructors with solutions to all exercises, and selected data sets, are available at http://www.crcpress.com/9781138593145.  

This book provides a unified, in-depth introduction to the multipredictor regression methods widely used in biostatistics.

Designed as a handy reference for statistical analysis, this book provides an overview of statistical methods and guidelines for interpretation of results.

While this book focuses on engineering problems, its step-by-step approach provides a nice introduction to programming and the built-in functions in MATLAB.

MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB in the neurosciences. Problem-based approach with many examples using real data.

Although published several years ago, this book remains a lively, concise introduction to the most popular and important features of MATLAB. Also available in print at Tisch Library.

An Essential Reference for intermediate and advanced R Programmers Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends.

The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to creating graphs from data sets. Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats.

Hugely successful and popular text presenting a comprehensive guide for all R users. Praise for the first edition: "if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R." (The American Statistician, August 2008).

This book helps you perform data analysis with R. Each recipe addresses a specific problem, with a discussion that explains the solution.

Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution.   Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks. Lander's self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You'll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you'll construct several complete models, both linear and nonlinear, and use some data mining techniques.   By the time you're done, you won't just know how to write R programs, you'll be ready to tackle the statistical problems you care about most.   Coverage Includes: Exploring R, RStudio, and R packages Using R for math: variable types, vectors, calling functions, and more Exploiting data structures, including data.frames, matrices, and lists Creating attractive, intuitive statistical graphics Writing user-defined functions Controlling program flow with if, ifelse, and complex checks Improving program efficiency with group manipulations Combining and reshaping multiple datasets Manipulating strings using R's facilities and regular expressions Creating normal, binomial, and Poisson probability distributions Programming basic statistics: mean, standard deviation, and t-tests Building linear, generalized linear, and nonlinear models Assessing the quality of models and variable selection Preventing overfitting, using the Elastic Net and Bayesian methods Analyzing univariate and multivariate time series data Grouping data via K-means and hierarchical clustering Preparing reports, slideshows, and web pages with knitr Building reusable R packages with devtools and Rcpp Getting involved with the R global community

Focusing on practical solutions, this book offers a crash course in statistics and covers methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods. Also presents R's graphical capabilities for exploring and presenting data.