Linear Regression Models Applications in R

This book includes chapters on specifying the correct linear regression model, adjusting for measurement error, understanding the effects of influential observations, and using multilevel data.

Info autore

John P. Hoffmann is a professor of sociology at Brigham Young University. He holds a PhD in Criminology from the State University of New York at Albany and a Masters of Public Health (MPH) from Emory University. He has worked at the U.S. Centers for Disease Control and Prevention (CDC) and the National Opinion Research Center (NORC) of the University of Chicago; and taught at Hokkaido University and the University of South Carolina. Hoffmann is the author of more than 100 journal articles and book chapters and 10 books on applied statistics, criminology, and the sociology of religion.

Riassunto

This book includes chapters on specifying the correct linear regression model, adjusting for measurement error, understanding the effects of influential observations, and using multilevel data.

Dettagli sul prodotto

• Autori: John P. Hoffmann, John P. Hoffman
• Editore: Taylor & Francis Ltd.
• Contenuto: Libro
• Forma del prodotto: Copertina rigida
• Data pubblicazione: 30.09.2021
• Categoria: Scienze umane, arte, musica > Psicologia > Fondamenti; Saggistica > Psicologia, esoterismo, spiritualità, antroposofia > Psicologia: tematiche generali, opere di consultaz; Scienze sociali, diritto, economia > Economia > Tematiche generali, enciclopedie; Scienze naturali, medicina, informatica, tecnica > Matematica > Teoria delle probabilità, stocastica, statistica m
• EAN: 9780367753689
• ISBN: 978-0-367-75368-9
• Numero di pagine: 420
• Serie: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
• Categorie: BUSINESS & ECONOMICS / Statistics, MATHEMATICS / Probability & Statistics / General, MATHEMATICS / Probability & Statistics / Regression Analysis, Economic statistics, Psychological methodology, Probability & statistics, Probability and statistics, Econometrics and economic statistics, Social Science Statistics, behavioral data analysis, graduate level statistics, advanced regression techniques in R, interaction effects modeling, multicollinearity detection, missing data treatment