Advanced Data Analysis from an Elementary Point of View

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, 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 2015. A copy of the next-to-final version will remain freely accessible here permanently.

Complete draft in PDF

Table of contents:

    I. Regression and Its Generalizations
  1. Regression Basics
  2. The Truth about Linear Regression
  3. Model Evaluation
  4. Smoothing in Regression
  5. Simulation
  6. The Bootstrap
  7. Weighting and Variance
  8. Splines
  9. Additive Models
  10. Testing Regression Specifications
  11. More about Hypothesis Testing
  12. Logistic Regression
  13. Generalized Linear Models and Generalized Additive Models
  14. Classification and Regression Trees
    II. Multivariate Data, Distribution Estimates, and Latent Structure
  15. Multivariate Distributions
  16. Density Estimation
  17. Relative Distributions and Smooth Tests
  18. Principal Components Analysis
  19. Factor Analysis
  20. Nonlinear Dimensionality Reduction
  21. Mixture Models
  22. Graphical Models
    III. Causal Inference
  23. Graphical Causal Models
  24. Identifying Causal Effects
  25. Estimating Causal Effects
  26. Discovering Causal Structure
    IV. Dependent Data
  27. Time Series
  28. Time Series with Latent Variables
  29. Simulation-Based Inference
  30. Longitudinal, Spatial and Network Data
    Problem Sets with Data

Planned changes:

(Text last updated 1 April 2015; this page last updated 1 April 2015)