Readings are listed by session. Core textbooks for the whole course are listed at the end.
Lecture 1: Probability Models, Random Variables, and Expectation
Casella and Berger, Statistical Inference; DeGroot and Schervish, Probability and Statistics; Wasserman, All of Statistics.
Lecture 2: Probability Distributions and Multivariate Models
Casella and Berger, Statistical Inference; DeGroot and Schervish, Probability and Statistics; Anderson, An Introduction to Multivariate Statistical Analysis.
Lecture 3: Sampling Distributions and Asymptotic Theory
van der Vaart, Asymptotic Statistics; Casella and Berger, Statistical Inference; Efron and Tibshirani, An Introduction to the Bootstrap.
Lecture 4: Point Estimation: Moments, Likelihood, and Optimality
Casella and Berger, Statistical Inference; Lehmann and Casella, Theory of Point Estimation; van der Vaart, Asymptotic Statistics.
Lecture 5: Confidence Intervals and Hypothesis Testing
Casella and Berger, Statistical Inference; Lehmann and Romano, Testing Statistical Hypotheses; Wasserstein and Lazar, “The ASA Statement on p-Values.”
Lecture 6: Bayesian Inference, Conjugacy, and Posterior Computation
Gelman et al., Bayesian Data Analysis; Robert, The Bayesian Choice; McElreath, Statistical Rethinking.
Lecture 7: Experimental Design, Randomization, and Blocking
Montgomery, Design and Analysis of Experiments; Fisher, The Design of Experiments; Imbens and Rubin, Causal Inference for Statistics, Social, and Biomedical Sciences.
Lecture 8: ANOVA, Linear Contrasts, and Multiple Comparisons
Montgomery, Design and Analysis of Experiments; Hsu, Multiple Comparisons; Kutner et al., Applied Linear Statistical Models.
Lecture 9: Linear Regression and Generalized Linear Models
McCullagh and Nelder, Generalized Linear Models; Agresti, Foundations of Linear and Generalized Linear Models; Dobson and Barnett, An Introduction to Generalized Linear Models.
Lecture 10: Nonparametric Inference, Rank Tests, and Resampling
Hollander, Wolfe, and Chicken, Nonparametric Statistical Methods; Wasserman, All of Nonparametric Statistics; Efron and Tibshirani, An Introduction to the Bootstrap.
Lecture 11: Statistical Power, Sample Size, and Equivalence
Cohen, Statistical Power Analysis for the Behavioral Sciences; Chow et al., Sample Size Calculations in Clinical Research; Lakens, Equivalence Tests.
Lecture 12: Linear Mixed Models and Hierarchical Data
Pinheiro and Bates, Mixed-Effects Models in S and S-PLUS; Gelman and Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models; McCulloch et al., Generalized, Linear, and Mixed Models.
Lecture 13: Multivariate Analysis: PCA, Covariance, and Dimension Reduction
Jolliffe and Cadima, “Principal Component Analysis: A Review and Recent Developments”; Johnson and Wichern, Applied Multivariate Statistical Analysis; Anderson, An Introduction to Multivariate Statistical Analysis.
Lecture 14: Statistical Modeling Workflow, Simulation, and Reproducibility
Gentle, Elements of Computational Statistics; Boos and Stefanski, Essential Statistical Inference; Peng, “Reproducible Research in Computational Science”; National Academies, Reproducibility and Replicability in Science.
Core textbooks
Casella and Berger, Statistical Inference; Lehmann and Romano, Testing Statistical Hypotheses; van der Vaart, Asymptotic Statistics; Gelman et al., Bayesian Data Analysis; Montgomery, Design and Analysis of Experiments; McCullagh and Nelder, Generalized Linear Models.