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Statistical Hypothesis Testing in Context

Statistical Hypothesis Testing in Context

Statistical Hypothesis Testing in Context Hardback - 2022

by Michael P. Fay

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Hardcover. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; With over 60 years of applied experience, Fay and Brittain present hypothesis testing and compatible confidence intervals, emphasize strategies to address the reproducibility crisis, and provide methods for proper causal interpretation
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Details

  • Title Statistical Hypothesis Testing in Context
  • Author Michael P. Fay
  • Binding Hardback
  • Condition New
  • Pages 448
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2022-05-05
  • Features Bibliography, Index
  • Bookseller's Inventory # ria9781108423564_inp
  • ISBN 9781108423564 / 1108423566
  • Weight 2.19 lbs (0.99 kg)
  • Dimensions 10 x 7.2 x 1.16 in (25.40 x 18.29 x 2.95 cm)
  • Category Mathematics
  • Library of Congress subjects Statistical hypothesis testing, MATHEMATICS / Probability & Statistics /
  • Library of Congress Catalogue Number 2021044789
  • Dewey Decimal Code 519.56
  • Quantity available 619

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Reader reviews for Statistical Hypothesis Testing in Context

From the publisher

Fay and Brittain present statistical hypothesis testing and compatible confidence intervals, focusing on application and proper interpretation. The emphasis is on equipping applied statisticians with enough tools - and advice on choosing among them - to find reasonable methods for almost any problem and enough theory to tackle new problems by modifying existing methods. After covering the basic mathematical theory and scientific principles, tests and confidence intervals are developed for specific types of data. Essential methods for applications are covered, such as general procedures for creating tests (e.g., likelihood ratio, bootstrap, permutation, testing from models), adjustments for multiple testing, clustering, stratification, causality, censoring, missing data, group sequential tests, and non-inferiority tests. New methods developed by the authors are included throughout, such as melded confidence intervals for comparing two samples and confidence intervals associated with Wilcoxon-Mann-Whitney tests and Kaplan-Meier estimates. Examples, exercises, and the R package asht support practical use.
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