Readings

Texts

G — Gailmard, Statistical Modeling and Inference for Social Science (Cambridge University Press, 2014).

A&M — Aronow & Miller, Foundations of Agnostic Statistics (Cambridge University Press, 2019).

Free companions

R4DS — Wickham, Çetinkaya-Rundel & Grolemund, R for Data Science, 2nd edition. https://r4ds.hadley.nz/

MtM — Arel-Bundock, Model to Meaning (CRC Press, 2026). https://marginaleffects.com/

Healy — Healy, Data Visualization: A Practical Introduction. https://socviz.co/

By week

Wk	Topic	Reading
1	Data and measurement	G 1, 2.1; R4DS Whole game
2	Describing empirical data	G 2.2–2.3; R4DS Exploratory data analysis, Factors, Missing values
3	Probability and data-generating processes	G 3–4; A&M 1.1–1.2
4	Expectation and the sample mean	G 5–6; A&M 1.3, 2
5	Estimation and large samples	G 7, 9.2; A&M 3
6	Inference	G 8, 9.1; A&M 3
7	Midterm exam	—
8	Linear regression I	G 2.3.5–2.3.6; A&M 4.1
9	Linear regression II	G 7.5; A&M 4.1–4.2
10	Linear regression III	G 2.3.7, 6.7; A&M 4.4; MtM Slopes
11	Inference for regression	G 8.5; A&M 4.2–4.3
12	Presenting and interpreting results	MtM I–II; Healy Work with Models; A&M 4.5, 7