Tuesdays and Thursdays, 10:30--11:50 Wean Hall 7500

The goal of this class is to train you in using statistical models to analyze data — as data summaries, as predictive instruments, and as tools for scientific inference. We will build on the theory and applications of the linear model, introduced in 36-401, extending it to more general functional forms, and more general kinds of data, emphasizing the computation-intensive methods introduced since the 1980s. After taking the class, when you're faced with a new data-analysis problem, you should be able to (1) select appropriate methods, (2) use statistical software to implement them, (3) critically evaluate the resulting statistical models, and (4) communicate the results of your analyses to collaborators and to non-statisticians.

During the class, you will do data analyses with existing software, and write your own simple programs to implement and extend key techniques. You will also have to write reports about your analyses.

**36-608** Graduate students from other departments wishing to
take this course should register for it under the number "36-608". Enrollment
for 36-608 is very limited, and by permission of the professors only.

36-401, with a grade of C or better. Exceptions are only granted for graduate students in other departments taking 36-608.

Professors | Cosma Shalizi | cshalizi [at] cmu.edu |

Baker Hall 229C | ||

Max G'Sell | mgsell [at] stat.cmu.edu | |

Baker Hall 132B | ||

Teaching assistants | Ms. Purvasha Chakravarti | |

Mr. Jaehyeok Shin | ||

Mr. Michael Spece | ||

Mr. Michael Stanley |

*Model evaluation*: statistical inference, prediction, and scientific inference; in-sample and out-of-sample errors, generalization and over-fitting, cross-validation; evaluating by simulating; the bootstrap; penalized fitting; mis-specification checks*Yet More Linear Regression*: what is regression, really?; what ordinary linear regression actually does; what it cannot do; extensions*Smoothing*: kernel smoothing, including local polynomial regression; splines; additive models; kernel density estimation*Generalized linear and additive models*: logistic regression; generalized linear models; generalized additive models.*Latent variables and structured data*: principal components; factor analysis and latent variables; latent cluster/mixture models; graphical models in general*Causality*: graphical causal models; causal inference from randomized experiments; identification of causal effects from observations; estimation of causal effects; discovering causal structure*Dependent data*: Markov models for time series without latent variables; hidden Markov models for time series with latent variables; smoothing and modeling for spatial and network data

The homework will give you practice in using the techniques you are learning
to analyze data, and to interpret the analyses. There will be 11 weekly
homework assignments, nearly one every week; they will all be due
on ~~Wednesdays~~ Thursdays at 11:59 pm (i.e., the
night ~~before~~ after Thursday classes), through Blackboard. All
homeworks count equally, totaling 60% of your grade. The lowest three homework
grades will be dropped; consequently, no late homework will be accepted for any
reason whatsoever.

Communicating your results to others is as important as getting good results
in the first place. Every homework assignment will require you to write about
that week's data analysis and what you learned from it. This portion of the
assignment will be graded, along with the other questions. As always, raw
computer output and R code is not acceptable; your document must be humanly
readable. You should submit an R
Markdown or knitr file, integrating
text, figures and R code; submit *both* your knitted file and the
source. If that is not feasible, contact the professors as soon as possible.
Microsoft Word files will not be graded.

For help on using R Markdown, see "Using R Markdown for Class Reports".

Unlike PDF or plain text, Word files do not display consistently across different machines, different versions of the program on the same machine, etc., so not using them eliminates any doubt that what we grade differs from what you think you wrote. Word files are also much more of a security hole than PDF or (especially) plain text. Finally, it is obnoxious to force people to buy commercial, closed-source software just to read what you write. (It would be obnoxious even if Microsoft paid you for marketing its wares that way, but it doesn't.)

There will be two take-home mid-term exams (10% each), due at 11:59 pm on March 3rd and April 21st. You will have one week to work on each midterm. There will be no homework in those weeks. These due dates will not be moved; please schedule job interviews and other extra-curricular activities around them. There will also be a take-home final exam (20%), due at 10:30 am on May 9th.

Exams must also be submitted through Blackboard, under the same rules about file formats as homework.

Direct any questions or complaints about your grades directly to the professors; the teaching assistants have no authority to make changes.

If you want help with computing, please bring your laptop.

Monday | 3:00--4:00 | Mr. Shin | Porter Hall 117 |

Tuesday | 2:30--3:30 | Mr. Spece | Porter Hall 117 |

Wednesday | 1:00--2:00 | Prof. Shalizi | Baker Hall 229A |

Wednesday | 4:30--5:30 | Mr. Stanley | Porter Hall 117 |

Thursday | 12:30--1:30 | Prof. G'Sell | Doherty Hall 2122 |

Thursday | 2:00--3:00 | Ms. Chakravarti | Porter Hall 117 |

Thursday | 3:00--4:00 | Prof. Shalizi | Baker Hall 229A |

If you cannot make office hours, please e-mail the professors about making an appointment.

The primary textbook for the course will be the
draft Advanced Data Analysis from an
Elementary Point of View. Chapters will be linked to here as they
become needed. You are expected to read these chapters, and are unlikely to be
able to do the assignments without doing so. (There will be a prize for the
student who identifies the most errors by 27 April, presented at the last class
meeting.) In addition, Paul Teetor, The R Cookbook (O'Reilly
Media, 2011,
ISBN 978-0-596-80915-7)
is **required** as a reference.

Cox and Donnelly, Principles of Applied Statistics (Cambridge
University Press, 2011,
ISBN 978-1-107-64445-8); Faraway, Extending
the Linear Model with R (Chapman Hall/CRC Press, 2006,
ISBN 978-1-58488-424-8; errata);
and Venables and Ripley, Modern Applied Statistics with S
(Springer,
2003;
ISBN 9780387954578)
will be **optional**. The campus bookstore should have copies of
all of these.

If you are unsure about what is or is not appropriate, please ask the professors before submitting anything; there will be no penalty for asking.

R is a free, open-source software
package/programming language for statistical computing. You should have begun
to learn it in 36-401 (if not before), and this class presumes that you have.
Almost every assignment will require you to use it. No other form of
computational work will be accepted. If you are *not* able to use R, or
do not have ready, reliable access to a computer on which you can do so, let me
know at once.

Here are some resources for learning R:

- The official intro, "An Introduction to R", available online in HTML and PDF
- John Verzani, "simpleR", in PDF
- Quick-R. This is primarily aimed at those who already know a commercial statistics package like SAS, SPSS or Stata, but it's very clear and well-organized, and others may find it useful as well.
- Patrick Burns, The R Inferno. "If you are using R and you think you're in hell, this is a map for you."
- Thomas Lumley, "R Fundamentals and Programming Techniques" (large PDF)
- Paul Teetor, The R Cookbook, explains how to use R to do many, many common tasks. (It's like the inverse to R's help: "What command does X?", instead of "What does command Y do?"). It is one of the required texts, and is available at the campus bookstore.
- The notes for 36-350, Introduction to Statistical Computing
- There are now many books about R. Some recommendable ones:
- Joseph Adler R in a Nutshell (O'Reilly, 2009; ISBN 9780596801700). Probably most useful for those with previous experience programming in another language.
- W. John Braun and Duncan J. Murdoch, A First Course in Statistical Programming with R (Cambridge University Press, 2008; ISBN 978-0-521-69424-7)
- John M. Chambers, Software for Data Analysis: Programming with R (Springer, 2008, ISBN 978-0-387-75935-7). The best book on writing clean and reliable R programs; probably more advanced than you will need.
- Norman Matloff, The Art of R Programming (No Starch Press, 2011, ISBN 978-1-59327-384-2). Good introduction to programming for complete novices using R. Less statistics than Braun and Murdoch, more programming skills.

Current revision of the complete textbook

- January 12 (Tuesday): Lecture 1, Introduction to the class; regression
*Reading*: Chapter 1 (PDF, selected R, 01.Rda data file for examples)*Optional reading*: Cox and Donnelly, chapter 1; Faraway, chapter 1 (especially up to p. 17).- Homework 1: assignment,
`CAPA.csv`data file - January 14 (Thursday): Lecture 2, The truth about linear regression
*Reading*: Chapter 2 (PDF, selected R)*Optional reading*: Faraway, rest of chapter 1- January 19 (Tuesday): Lecture 3, Evaluation of Models: Error and inference
*Reading*: Notes, chapter 3 (PDF, selected R)*Optional reading*: Cox and Donnelly, ch. 6*Handout*: "`predict`and Friends: Common Methods for Predictive Models in R" (PDF, R Markdown)- January 21 (Thursday): Lecture 4, Smoothing methods in regression
*Reading*: Chapter 4 (PDF, selected R)*Optional readings*: Faraway, section 11.1; Hayfield and Racine, "Nonparametric Econometrics: The`np`Package"; Gelman and Pardoe, "Average Predictive Comparisons for Models with Nonlinearity, Interactions, and Variance Components" [PDF]- Homework 1 due (at 11:59 pm the night before)
- Homework 2: assignment, data file, starter code
- January 26 (Tuesday): Lecture 5, Writing R Code
- In-class examples: knitted HTML, R Markdown
*Reading*: Appendix on writing R code (PDF, R for selected examples)- January 28 (Thursday): Lecture 6, Simulation
- In-class examples: commented R file
*Reading*: Chapter 5 (PDF, R for selected examples)- Homework 2 due (at 11:59 pm the night before)
- Homework 3: assignment,
`stock_history.csv`data file - February 2 (Tuesday): Lecture 7, The Bootstrap
*Reading*: Chapter 6 (PDF, R for selected examples)*Optional reading*: Cox and Donnelly, chapter 8- February 4 (Thursday): Lecture 8, Heteroskedasticity, weighted least squares, and variance estimation
*Reading*: Chapter 7 (PDF, R for selected examples)*Optional reading*: Faraway, section 11.3- Homework 3 due (at 11:59 pm the night before)
- Homework 4: assignment,
`nampd.csv`data set,`MoM.txt`data set - February 9 (Tuesday): Lecture 9, Splines
*In-class examples*: HTML, Rmd*Reading*: Chapter 8 (PDF, R for selected examples)*Optional reading*: Faraway, section 11.2- February 11 (Thursday): Lecture 10, Additive models
*Reading*: Chapter 9 (PDF, R for selected examples)*Optional reading*: Faraway, chapter 12- Homework 4 due (at 11:59 pm)
- Homework 5: assignment,
`gmp-2006.csv` - February 16 (Tuesday): Lecture 11, Testing Regression Specifications
*Reading*: Chapter 10 (PDF, R for selected examples)- In-class demo: knitted HTML, R Markdown source file
*Optional reading*: Cox and Donnelly, chapter 7- February 18 (Thursday): Lecture 12, Logistic Regression
*Reading*: Chapter 11 (PDF, R)*Optional reading*: Faraway, chapter 2 (omitting sections 2.11 and 2.12)- Homework 5 due (at 11:59 pm)
- Homework 6: assignment,
`ch.csv`data file - February 23 (Tuesday): Lecture 13, Generalized linear models and generalized additive models
*Reading*: Chapter 12 (PDF)*Optional reading*: Faraway, section 3.1 and chapter 6- February 25 (Thursday): Lecture 14, GLMs and GAMs continued
*Reading*and*optional reading*: Same as lecture 13- Homework 6 due (at 11:59 pm)
- Exam 1: assignment,
`RAJ.csv` - March 1 (Tuesday): Lecture 15, Multivariate Distributions
*Reading*: Appendix on multivariate distributions (PDF)- March 3 (Thursday): Lecture 16, Density Estimation
*Reading*: Chapter 14 (PDF)- Exam 1 due (at 11:59 pm)
~~Homework 7 assigned~~- March 8 and 10: Spring break
- March 15 (Tuesday): Lecture 17, Principal Components Analysis
*Reading*: Chapter 16 (PDF)- March 17 (Thursday): Lecture 18, Factor Models
*Reading*: Chapter 17 (PDF)~~Homework 7 due (at 11:59 pm)~~- Homework 8: assignment,
`stockData.RData`file - March 22 (Tuesday): Lecture 19, Mixture Models
*Reading*: Chapter 19 (PDF, R for selected examples)- March 24 (Thursday): Lecture 20, Missing Data
*Reading*: TBD*Optional reading*: Cox and Donnelly, chapter 5- Homework 8 due (at 11:59 pm)
- Homework 9: assignment
- March 29 (Tuesday): Lecture 21, Graphical Models
*Reading*: Chapter 20 (PDF)- March 31 (Thursday): Lecture 22, Graphical Causal Models
*Reading*: Chapter 24 (PDF)*Optional reading*: Cox and Donnelly, chapters 6 and 9; Pearl, "Causal Inference in Statistics", section 1, 2, and 3 through 3.2- Homework 9 due (at 11:59 pm)
- Homework 10: assignment, data file
- April 5 (Tuesday): Lecture 23, Identifying Causal Effects from Observations
*Reading*: Chapter 25 (PDF)*Optional reading*: Pearl, "Causal Inference in Statistics", sections 3.3--3.5, 4, and 5.1- April 7 (Thursday): Lecture 24, Estimating Causal Effects from Observations
*Reading*: Chapter 27 (PDF)- Homework 10 due (at 11:59 pm)
- Exam 2: assignment,
`paristan.csv` - April 12 (Tuesday): Lecture 25, Discovering Causal Structure from Observations
*Reading*: Chapter 28 (PDF)- April 14 (Thursday): Carnival, no class
- April 19 (Tuesday): Lecture 26, Time Series I
*Reading*: Chapter 21 (PDF)- April 21 (Thursday): Lecture 27, Time Series II
- Reading: Chapter 21 (PDF)
- Exam 2 due (at 11:59 pm)
- Homework 11: assignment; for data set, see homework 5
- April 26 (Tuesday): Lecture 28, Survival Analysis
- April 28 (Thursday): Lecture 29, Principles
- Homework 11 due (at 11:59 pm)
- Exam 3: assignment,
`macro.csv` - May 9 (Monday)
- Final exam due at
**10:30 am**