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by Cosma Rohilla Shalizi
This is a draft textbook on data analysis methods, intended for a one-semester course for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression. It began as the lecture notes for 36-402 at Carnegie Mellon University.
By making this draft generally available, I am not promising to provide any assistance or even clarification whatsoever. Comments are, however, generally welcome.
The book is under contract to Cambridge University Press; it should be turned over to the press at the end of 2013 or beginning of 2014 in early before the end of 2015 by the end of 2018 2019, inshallah when I can manage. A copy of the next-to-final version will remain freely accessible here permanently.
What you're probably looking for
Directory of chapter-by-chapter R files for examples
Directory of data sets used in examples
Table of contents
- Regression Basics
- The Truth about Linear Regression
- Model Evaluation
- Smoothing in Regression
- Simulation
- The Bootstrap
- Splines
- Additive Models
- Testing Regression Specifications
- Weighting and Variance
- Logistic Regression
- Generalized Linear Models and Generalized Additive Models
- Classification and Regression Trees
II. Distributions and Latent Structure - Density Estimation
- Principal Components Analysis
- Factor Models
- Mixture Models
- Graphical Models
III. Causal Inference - Graphical Causal Models
- Identifying Causal Effects
- Estimating Causal Effects
- Discovering Causal Structure
IV. Dependent Data - Time Series
- Simulation-Based Inference
Online-only Appendices- Big O and Little o Notation
- Taylor Expansions
- Propagation of Error, and Standard Errors for Derived Quantities
- Optimization
- Relative Distributions and Smooth Tests of Goodness of Fit
- Nonlinear Dimensionality Reduction
- Rudimentary Graph Theory
- Missing Data
- Writing R Functions
Data-Analysis Assignments
I. Regression and Its Generalizations
Planned changes
- Remove redundant versions of the data-analysis assignments; provide solutions as a separate document through publisher
- Unified treatment of information theory as an appendix
- Improved (=correct) treatment of nonparametric instrument variables
- Trim time-series chapter so it's less of a catalog of everything that might be useful
- Break out stuff on heuristic essential asymptotics as a separate appendix
- Make sure notation is consistent throughout: insist that vectors are always matrices, or use more geometric notation?
- Figure out how to cut at least 50 pages
- Index: currently (8 February 2025) done for most chapters but not proofed or unified, and possibly missing for some sections... Revisit to standardize terms and limit levels of hierarchy
(Text last updated 8 February 2025; this page last updated 15 January 2024)
