Schedule

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Summary

This schedule is subject to change, so please check it regularly. We will notify the class of changes that affect the upcoming week or any other major changes…

Week	Topic(s)
1	Introduction
2	Exploratory Data Analysis
3	Random Variables and Likelihood
4	Data Wrangling
5	Frequentist Inference
6	Bayesian Inference
7	Numerical Methods for Likelihood
8	Data Resampling
9	Statistical Modeling I
10	Statistical Modeling II
11	High-Dimensional Inference
12	Latent Variable Models

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Details

Week 1

Topics:

• Introduction to statistics, data science, and the course
• Data sets used in the course
• R
• Reproducible research through R Markdown

Reading: Parts I and II of FAS

Supplemental Reading: R4DS Ch. 1, 4, 6, 8, 17-21; DALS Ch. “Getting Started”

Week 2

Topics:

• Exploratory Data Analysis overview
• Quantitative summaries of data
• Data visualization
• Principal components analysis (dimension reduction)

Reading: Part IV of FAS

Supplemental Reading: R4DS Ch. 2, 3, 7; DALS Ch. “Exploratory Data Analysis”

Week 3

Topics:

• Probability
• Random variables
• Probability distributions
• Likelihood
• Exponential family distributions

Reading: Part V of FAS

Supplemental Reading: AOS Ch. 1-3, 9.3, 9.13.2, 9.13.3

Week 4

Topics:

• Data wrangling
• Tidy data
• dplyr package

Reading: Part III of FAS

Supplemental Reading: R4DS Ch. 5, 9-16

Week 5

Topics:

• Frequentist inference
• Hypothesis testing
• Point estimation
• Maximum likelihood estimation
• Exponential family distributions

Reading: Part VI of FAS

Supplemental Reading: AOS Ch. 9, 10.1-10.4, 10.6, 10.8-10.10

Week 6

Topics:

• Bayesian inference
• Estimation
• Classification
• Prior distributions

Reading: Part VII of FAS

Supplemental Reading: AOS Ch. 11

Week 7

Topics:

• Likelihood based hypothesis testing (catch-up from last week)
• Numerical methods for likelihoods
• EM algorithm
• Markov chain Monte Carlo

Reading: Part VIII of FAS

Supplemental Reading: AOS 9.13.4; ESL Ch. 8.5 van Ravenzwaaij et al. (2016)

Week 8

Topics:

• Nonparametric statistics
• Empirical distribution functions
• Bootstrap
• Permutation methods
• Goodness of fit

Reading: Part IX of FAS

Supplemental Reading: AOS Ch. 9.2 and 10.5; NPS Ch. 2-3

Week 9

Topics:

• Statistical modeling
• Simple linear regression
• Ordinary least squares
• Generalized least squares

Reading: Part X, Chapters 75-80, of FAS

Supplemental Reading: AOS Ch. 13; DALS Ch. “Linear Models”

Week 10

Topics:

• Generalized linear models
• Nonparametric regression
• Generalized additive models
• Bootstrapping model fits

Reading: Part X, Chapters 65-74, of FAS

Supplemental Reading: ESL Ch. 5.1-5.6, 7, 9.1; NPS Ch. 4.1-4.3, 5.1-5.5

Week 11

Topics:

• High-dimensional data and models
• Shrinkage and empirical Bayes
• False discovery rates
• Ridge regression
• Lasso regression

Reading: Part XI of FAS

Supplemental Reading: AOS Ch. 10.7, 12.7; DALS Ch. “Inference for HD Data”; Stigler (1990); Storey and Tibshirani (2003)

Week 12

Topics:

• Causal inference
• High-dimensional latent variable models
• Jackstraw
• Surrogate variable analysis

Reading: Part XII of FAS

Supplemental Reading: Holland (1986); Chung and Storey (2014); Leek and Storey (2007)

Text book abbreviations used above

Abbreviation	Full Title
AOS	All of Statistics
NPS	All of Nonparametric Statistics
DALS	Data Analysis for the Life Sciences
ESL	The Elements of Statistical Learning, 2nd Ed.
FAS	Foundations of Applied Statistics
R4DS	R for Data Science

Notes

• AOS and NPS chapter numbers refer to those in the PDF files available through Princeton University Library.
• DALS chapter titles refer to those from the Leanpub edition.
• Please obtain the most recent version of ESL from the authors’ web site.
• R4DS chapter numbers refer to those at http://r4ds.had.co.nz, NOT the printed book chapter numbers.

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Homework due dates

Assignment	Due
Homework 1	11:59PM on Feb. 21
Homework 2	11:59PM on Mar. 7
Homework 3	11:59PM on Mar. 28
Homework 4	11:59PM on Apr. 11
Homework 5	11:59PM on Apr. 25
Take Home Final Exam	11:59PM on May 14