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A Students Guide Stats Using R 1E: SAGE LTD

Autor Andrews Justice
en Paperback – 26 dec 2026

Din seria SAGE LTD

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Specificații

ISBN-13: 9781529602005
ISBN-10: 1529602009
Dimensiuni: 152 x 229 mm
Greutate: 0.34 kg
Editura: Sage Publications - IPS UK
Colecția SAGE LTD
Seria SAGE LTD


Notă biografică

Mark Andrews is an Associate Professor of Statistical Methods in the Department of Psychology at Nottingham Trent University. He teaches statistics to undergraduate and postgraduate students and is the course leader for the MSc in Behavioural Data Science. He also teaches advanced training courses on statistical methods, data science, and machine learning using R and Python.

Mark has a PhD and MSc in Cognitive Science from Cornell University and was previously a postdoctoral research fellow at University College London, working first in the Gatsby Computational Neuroscience Unit and later in the Division of Psychology and Language Sciences. His research interests include statistical methods in the social and behavioural sciences, computational cognitive science and neuroscience, and the application of mathematical and statistical models to understanding human cognition.

Mark was Chair of the British Psychological Society's Mathematical, Statistical, and Computing Psychology section and is currently deputy chair of the BPS Statistics and Research Methods Advisory Panel. He is also a committee member of the Royal Statistical Society's section on teaching statistics. He is the author of "Doing Data Science in R: An Introduction for Social Scientists" (SAGE, 2021).


Cuprins

Part I: Foundations
Chapter 1: Introducing statistics
Chapter 2: Introducing R & RStudio
Chapter 3: Exploratory data analysis
Chapter 4: Introducing inference
Chapter 5: Data wrangling
Part II: Linear models and friends
Chapter 6: Normal models
Chapter 7: Simple linear regression
Chapter 8: Multiple linear regression
Chapter 9: ANOVA and general linear models
Chapter 10: Repeated measures analysis
Chapter 11: Multilevel and mixed effects models
Chapter 12: Logistic regression
Chapter 13: Models for count data
Part III: Bayesian methods
Chapter 14: Bayesian data analysis