The Leading eBooks Store Online

for your Apple or Android device, Nook, Kobo, PC, Mac, Sony Reader...

New to eBooks.com?

Learn more
Browse our categories
  • Bestsellers - This Week
  • Foreign Language Study
  • Pets
  • Bestsellers - Last 6 months
  • Games
  • Philosophy
  • Archaeology
  • Gardening
  • Photography
  • Architecture
  • Graphic Books
  • Poetry
  • Art
  • Health & Fitness
  • Political Science
  • Biography & Autobiography
  • History
  • Psychology & Psychiatry
  • Body Mind & Spirit
  • House & Home
  • Reference
  • Business & Economics
  • Humor
  • Religion
  • Children's & Young Adult Fiction
  • Juvenile Nonfiction
  • Romance
  • Computers
  • Language Arts & Disciplines
  • Science
  • Crafts & Hobbies
  • Law
  • Science Fiction
  • Current Events
  • Literary Collections
  • Self-Help
  • Drama
  • Literary Criticism
  • Sex
  • Education
  • Literary Fiction
  • Social Science
  • The Environment
  • Mathematics
  • Sports & Recreation
  • Family & Relationships
  • Media
  • Study Aids
  • Fantasy
  • Medical
  • Technology
  • Fiction
  • Music
  • Transportation
  • Folklore & Mythology
  • Nature
  • Travel
  • Food and Wine
  • Performing Arts
  • True Crime
  • Foreign Language Books
Multiple comparisons (Statistics)
  • 1
  • Page

Most popular at the top

  • Applied Multiway Data Analysisby Pieter M. Kroonenberg

    John Wiley & Sons, Inc. 2008; US$ 148.00

    From a preeminent authority—a modern and applied treatment of multiway data analysis This groundbreaking book is the first of its kind to present methods for analyzing multiway data by applying multiway component techniques. Multiway analysis is a specialized branch of the larger field of multivariate statistics that extends the standard methods for two-way data, such as component analysis, factor analysis, cluster analysis, correspondence analysis, and multidimensional scaling to multiway data. Applied Multiway Data Analysis presents a unique, thorough, and authoritative treatment of this relatively new and emerging approach to data analysis that is applicable across a range of fields, from the social and behavioral sciences to agriculture,... more...

  • Multilevel Modelingby Steven P. Reise; Naihua Duan

    Lawrence Erlbaum Associates 2002; US$ 49.95

    This book appeals to researchers who work with nested data structures or repeated measures data, including biomed & health researchers, clinical/intervention researchers and developmental & educational psychologists. Also some potential as a grad lvl tex more...

  • Multiple Comparisons Using Rby Frank Bretz

    CRC Press 2010; US$ 79.95

    Adopting a unifying theme based on maximum statistics, Multiple Comparisons Using R describes the common underlying theory of multiple comparison procedures through numerous examples. It also presents a detailed description of available software implementations in R. The R packages and source code for the analyses are available at http://CRAN.R-project.org After giving examples of multiplicity problems, the book covers general concepts and basic multiple comparisons procedures, including the Bonferroni method and Simes' test. It then shows how to perform parametric multiple comparisons in standard linear models and general parametric models. It also introduces the multcomp package in R, which offers a convenient interface to perform multiple... more...

  • Probabilistic Databasesby Dan Suciu; Dan Olteanu

    Morgan & Claypool Publishers 2011; US$ 45.00

    Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it... more...

  • 1
  • Page