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Statistical Hypothesis Testing in Context: Volume 52: Reproducibility, Inference, and Science (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 52)

Statistical Hypothesis Testing in Context: Volume 52: Reproducibility, Inference, and Science (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 52)

Statistical Hypothesis Testing in Context: Volume 52: Reproducibility,
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Statistical Hypothesis Testing in Context: Volume 52: Reproducibility, Inference, and Science (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 52) Hardback - 2022

by Brittain, Erica H

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  • Title Statistical Hypothesis Testing in Context: Volume 52: Reproducibility, Inference, and Science (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 52)
  • Author Brittain, Erica H
  • Binding Hardback
  • Condition Used - Good
  • Pages 448
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2022-05-05
  • Features Bibliography, Index
  • Bookseller's Inventory # 1108423566.G
  • 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 1

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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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