Online Public Access Catalogue (OPAC)
Library,Documentation and Information Science Division

“A research journal serves that narrow

borderland which separates the known from the unknown”

-P.C.Mahalanobis


Image from Google Jackets

Applications of regression for categorical outcomes using R/ Melamed, David & Doan, Long

By: Contributor(s): Publication details: Boca Raton: CRC Press, 2024Description: xv, 222 pages, charts, graphs 23.5 cmISBN:
  • 9781032509518
Subject(s): DDC classification:
  • 23 SA.06 M517
Contents:
Introducton -- Introduction to R studio and packages -- Overview of OLS regression and introduction to the generalized linear model -- describing categorical variables and some useful tests of association -- Regression for binary outcomes -- regression for binary outcomes- moderation and squared terms -- Regression for ordinal outcomes -- Regression for nominal outcomes -- Regression for count outcomes -- Additional outcomes types -- Special topics: comparing between models and missing data
Summary: This book covers the main models within the GLM (i.e., logistic, Poisson, negative binomial, ordinal, and multinomial). For each model, estimations, interpretations, model fit, diagnostics, and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata, SPSS and SAS, to using R, and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge, and for Quantitative social scientists due to it’s ability to act as a practitioners guide.
Tags from this library: No tags from this library for this title. Log in to add tags.

Includes bibliography and index

Introducton -- Introduction to R studio and packages -- Overview of OLS regression and introduction to the generalized linear model -- describing categorical variables and some useful tests of association -- Regression for binary outcomes -- regression for binary outcomes- moderation and squared terms -- Regression for ordinal outcomes -- Regression for nominal outcomes -- Regression for count outcomes -- Additional outcomes types -- Special topics: comparing between models and missing data

This book covers the main models within the GLM (i.e., logistic, Poisson, negative binomial, ordinal, and multinomial). For each model, estimations, interpretations, model fit, diagnostics, and how to convey results graphically are provided. There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis. Many in the social sciences are transitioning away from using Stata, SPSS and SAS, to using R, and this book uses statistical models which are relevant to the social sciences. Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge, and for Quantitative social scientists due to it’s ability to act as a practitioners guide.

There are no comments on this title.

to post a comment.
Library, Documentation and Information Science Division, Indian Statistical Institute, 203 B T Road, Kolkata 700108, INDIA
Phone no. 91-33-2575 2100, Fax no. 91-33-2578 1412, ksatpathy@isical.ac.in