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. 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
    II. Multivariate Data, Distribution Estimates, and Latent Structure
  14. Multivariate Distributions
  15. Density Estimation
  16. Relative Distributions and Smooth Tests
  17. Principal Components Analysis
  18. Factor Analysis
  19. Mixture Models
  20. Graphical Models
    III. Causal Inference
  21. Graphical Causal Models
  22. Identifying Causal Effects
  23. Estimating Causal Effects
  24. Discovering Causal Structure
    IV. Dependent Data
  25. Time Series
  26. Time Series with Latent Variables
  27. Longitudinal, Spatial and Network Data

Planned changes:

(Text last updated 16 April 2013; webpage last updated 16 April 2013)