A Student's Guide to Statistics Using R

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). Lucy Justice is a Principal Lecturer in the School of Social Sciences at Nottingham Trent University. She completed her PhD in quantitative psychology at the University of Leeds in 2012, followed by four years working as a statistician in the private sector. After returning to academia, she worked as a Senior Lecturer in NTU Psychology, focusing on statistical modelling of psychological phenomena and implementing best practices for teaching statistics. In 2022, she moved to a Principal Lecturer position in the School of Social Sciences, where she works to improve student outcomes using computational statistics and machine learning. Lucy's research interests include quantitative methods in psychology and social science, with particular attention to the application of statistical modelling techniques to psychological and social phenomena. Her work spans both methodological research in statistical analysis and substantive research on memory and cognition. She has co-authored book chapters on statistical methods in forensic psychology and has published research on memory perspective, flashbulb memories, and short-term memory development. She has also contributed to applied research using statistical methods to analyze profiles for deaths under probation supervision in England and Wales.

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