Mathematical Statistics and Data Analysis

Author: John A. Rice

Publisher: Cengage Learning

ISBN: 0534399428

Category: Mathematics

Page: 688

View: 4327


This is the first text in a generation to re-examine the purpose of the mathematical statistics course. The book's approach interweaves traditional topics with data analysis and reflects the use of the computer with close ties to the practice of statistics. The author stresses analysis of data, examines real problems with real data, and motivates the theory. The book's descriptive statistics, graphical displays, and realistic applications stand in strong contrast to traditional texts that are set in abstract settings. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition

Author: Sorin Drăghici

Publisher: CRC Press

ISBN: 1439809755

Category: Science

Page: 1036

View: 3272


Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that teaches students the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems. New to the Second Edition Completely updated and double the size of its predecessor, this timely second edition replaces the commercial software with the open source R and Bioconductor environments. Fourteen new chapters cover such topics as the basic mechanisms of the cell, reliability and reproducibility issues in DNA microarrays, basic statistics and linear models in R, experiment design, multiple comparisons, quality control, data pre-processing and normalization, Gene Ontology analysis, pathway analysis, and machine learning techniques. Methods are illustrated with toy examples and real data and the R code for all routines is available on an accompanying CD-ROM. With all the necessary prerequisites included, this best-selling book guides students from very basic notions to advanced analysis techniques in R and Bioconductor. The first half of the text presents an overview of microarrays and the statistical elements that form the building blocks of any data analysis. The second half introduces the techniques most commonly used in the analysis of microarray data.

Statistical and Machine-Learning Data Mining:

Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition

Author: Bruce Ratner

Publisher: CRC Press

ISBN: 1351652389

Category: Computers

Page: 662

View: 4302


Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. What is new in the Third Edition: The current chapters have been completely rewritten. The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

Global Ecosystems Database, Version 1.0 (on CD-ROM)

EPA Global Climate Research Program, NOAA/NGDC Global Change Database Program : User's Guide

Author: John J. Kineman,National Geophysical Data Center

Publisher: N.A


Category: Climatic changes

Page: 121

View: 9574


"The US Environmental Protection Agency (EPA), Environmental Research Laboratory - Corvallis, Oregon (ERL-C), established an Interagency Agreement with the US National Oceanic and Atmospheric Administration (NOAA), National Geophysical Data Center (NGDC) in September 1990. This agreement began a five year cooperative effort to develop a geographic database for modeling terrestrial climatebiosphere interactions in support of EPA's Global Climate Research Program. Although performing specific tasks under contract to the US EPA, NGDC independently operates a Global Change Database Program (GCDP) as part of its NOAA mission Considerable synergism therefore exists between the tasks performed for the JPA under the "Global Ecosystems Database Project,'' and other activities supporting NOAA Climate and Global Change Program"--Preface.

Handbook of Statistical Analysis and Data Mining Applications

Author: Robert Nisbet,John Elder,Gary Miner

Publisher: Academic Press

ISBN: 9780080912035

Category: Mathematics

Page: 864

View: 907


The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions. Written "By Practitioners for Practitioners" Non-technical explanations build understanding without jargon and equations Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models Practical advice from successful real-world implementations Includes extensive case studies, examples, MS PowerPoint slides and datasets CD-DVD with valuable fully-working 90-day software included: "Complete Data Miner - QC-Miner - Text Miner" bound with book

Doing Bayesian Data Analysis

A Tutorial with R, JAGS, and Stan

Author: John Kruschke

Publisher: Academic Press

ISBN: 0124059163

Category: Mathematics

Page: 776

View: 8519


Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets. The book is divided into three parts and begins with the basics: models, probability, Bayes’ rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment. This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business. Accessible, including the basics of essential concepts of probability and random sampling Examples with R programming language and JAGS software Comprehensive coverage of all scenarios addressed by non-Bayesian textbooks: t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis) Coverage of experiment planning R and JAGS computer programming code on website Exercises have explicit purposes and guidelines for accomplishment Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs

Statistical Methods for Trend Detection and Analysis in the Environmental Sciences

Author: Richard Chandler,Marian Scott

Publisher: John Wiley & Sons

ISBN: 111999196X

Category: Mathematics

Page: 388

View: 4550


The need to understand and quantify change is fundamental throughout the environmental sciences. This might involve describing past variation, understanding the mechanisms underlying observed changes, making projections of possible future change, or monitoring the effect of intervening in some environmental system. This book provides an overview of modern statistical techniques that may be relevant in problems of this nature. Practitioners studying environmental change will be familiar with many classical statistical procedures for the detection and estimation of trends. However, the ever increasing capacity to collect and process vast amounts of environmental information has led to growing awareness that such procedures are limited in the insights that they can deliver. At the same time, significant developments in statistical methodology have often been widely dispersed in the statistical literature and have therefore received limited exposure in the environmental science community. This book aims to provide a thorough but accessible review of these developments. It is split into two parts: the first provides an introduction to this area and the second part presents a collection of case studies illustrating the practical application of modern statistical approaches to the analysis of trends in real studies. Key Features: Presents a thorough introduction to the practical application and methodology of trend analysis in environmental science. Explores non-parametric estimation and testing as well as parametric techniques. Methods are illustrated using case studies from a variety of environmental application areas. Looks at trends in all aspects of a process including mean, percentiles and extremes. Supported by an accompanying website featuring datasets and R code. The book is designed to be accessible to readers with some basic statistical training, but also contains sufficient detail to serve as a reference for practising statisticians. It will therefore be of use to postgraduate students and researchers both in the environmental sciences and in statistics.

Learning Data Analysis with Datadesk Student Version 6.0

Author: Paul F. Velleman

Publisher: Addison-Wesley

ISBN: 9780201571240


Page: 384

View: 333


DataDesk® is a highly graphical and interactive statistical software program for the Macintosh computer. Originally developed at Cornell University, it is now used by thousands of institutions and individuals around the world for teaching and research. Combining the concepts of Exploratory Data Analysis with a full range of traditional statistical techniques, DataDesk's dynamic displays, drag-and-drop interface, and linked plots give users exciting insights into the nature and uses of data. DataDesk 5.0 Student Version is an excellent software package for undergraduate statistics courses. It covers all statistical and graphical methods needed at this level of instruction. The accompanying book, Learning Data Analysis with DataDesk by Paul Velleman, contains numerous worked-out examples, and more than 200 exercises with the easy-to-learn and user friendly DataDesk Software.