BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS

Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS

Statistical Methods for Mediation, Confounding and Moderation Analysis Using R
Stock photo: cover may vary

Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS Hardback - 2022

by Qingzhao Yu, Bin Li

Add to wish list
  • Used
New

Description

like new.
Ask the seller a question Add to wish list
A$315.72
A$5.66 Delivery within USA
Standard delivery: 2 to 14 days
More delivery options
Ships from GreatBookPrices (Maryland, United States)
The BIBLIO Guarantee

Details

  • Title Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS
  • Author Qingzhao Yu, Bin Li
  • Binding Hardback
  • Condition New
  • Pages 294
  • Volumes 1
  • Language ENG
  • Publisher CRC Press
  • Publication date 2022-03-21
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # 43753203
  • ISBN 9780367365479 / 0367365472
  • Weight 1.3 lbs (0.59 kg)
  • Dimensions 9.21 x 6.14 x 0.69 in (23.39 x 15.60 x 1.75 cm)
  • Category Mathematics
  • Library of Congress subjects Statistics - Data processing, SAS (Computer program language)
  • Library of Congress Catalogue Number 2021046570
  • Dewey Decimal Code 519.502
  • Quantity available 5

About GreatBookPrices Maryland, United States

Biblio member since 2024

Since 1991, we have worked every day to serve our customers with state-of-the-art technology and world class service. We are dedicated to providing customers around the world with the widest selection of books, DVDs, and CDs at the absolute lowest price.

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from GreatBookPrices

Reader reviews for Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS

Write a review for this book

There are no reviews for this book yet. Be the first to write a review!

Your review will be anonymous unless you login or create an account first.

Overall rating
Remember: You’re rating the book as a work, not the seller or the condition of the specific copy you purchased!

Important Terms and Guidelines

  • Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
  • Reviews must be at least 140 characters in length.
  • Please do not reveal critical plot elements.
  • This is not a help line. Contact customer support if you need help.
Your review must not include:
  • Obscenities, discriminatory language, or other insulting language not suitable for public domain
  • Advertisements, “spam” content, or references to other products, offers or websites.
  • Email addresses, URLs, phone numbers, physical addresses or other contact information.
  • Overly critical comments about other reviews or reviewers
  • Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
  • Availability, price, or alternative ordering/shipping information

From the publisher

Third-variable effect refers to the effect transmitted by third-variables that intervene in the relationship between an exposure and a response variable. Differentiating between the indirect effect of individual factors from multiple third-variables is a constant problem for modern researchers.

Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS introduces general definitions of third-variable effects that are adaptable to all different types of response (categorical or continuous), exposure, or third-variables. Using this method, multiple third- variables of different types can be considered simultaneously, and the indirect effect carried by individual third-variables can be separated from the total effect. Readers of all disciplines familiar with introductory statistics will find this a valuable resource for analysis.

Key Features:

  • Parametric and nonparametric method in third variable analysis
  • Multivariate and Multiple third-variable effect analysis
  • Multilevel mediation/confounding analysis
  • Third-variable effect analysis with high-dimensional data Moderation/Interaction effect analysis within the third-variable analysis
  • R packages and SAS macros to implement methods proposed in the book

About the author

Qingzhao Yu is Professor in Biostatistics, Louisiana State University Health Sciences Center.

Bin Li is Associate Professor in Statistics, Louisiana State University.

Please wait a moment while we load the requested page.

tracking-