Section A
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 In previous years, a small number of well-prepared graduate students from other departments have been allowed to take this course, by registering for it as 36-608. (Graduate students enrolling in 36-402 will be dropped automatically from the roster.) This year, because of the number of undergraduate students needing to take 402, we have no resources to accommodate students wishing to take 608 for a grade. If space is available in the classroom, a few may be allowed to audit the course.
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 | ||
| Teaching assistants | Mr. Niccolo Dalmasso | |
| Mr. Alan Mishler | ||
| Mr. Michael Spece Ibanez | ||
| Mr. Lee Richardson |
Homework will be 50% of the grade, a midterms exam 20%, and the final
exam 30%.
The homework will give you practice in using the techniques you are learning to analyze data, and to interpret the analyses. There will be 12 weekly homework assignments, nearly one every week; they will all be due on Wednesdays at 11:59 pm (i.e., the night before Thursday classes), through Blackboard. All homeworks count equally, totaling 50% 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 writing is part of the assignment and will be graded. 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 me as soon as possible. Microsoft Word files get an automatic grade of 0, with no feedback*.
For help on using R Markdown, see "Using R Markdown for Class Reports".
There will be a take-home mid-term exam (20% of your final grade), due at 11:59 pm on Wednesday, 8 March. You will have one week to work on the midterm, and there will be no homework that week. There will also be a take-home final exam (30%), due at 10:30 am on Monday, 8 May. These due date will not be moved once the semester begins; please schedule job interviews and other extra-curricular activities around them.
The exams may require you to use any material already covered in the readings, lectures or assignments. All exams will be cumulative.
Exams must also be submitted through Blackboard, under the same rules about file formats as homework.
The purpose of this course is to help you learn data analysis. The purpose of the assignments is to help you learn by giving you structured opportunities for practice. The purpose of grading is primarily to give you feedback, distinguishing what you did well on from what you should work on improving.
The exams will each be curved separately to ensure that they are comparable in scale to the homework before calculating your final grade. You should not presume that an un-curved average of 90 guarantees you an A.
If you believe that particular assignment has been incorrectly graded, tell me as soon as possible. Direct any questions or complaints about your grades to me; the teaching assistants have no authority to make changes. (This also goes for your final letter grade.) Complaints that the thresholds for letter grades are unfair, that you deserve a higher grade, etc., will accomplish much less than pointing to concrete problems in the grading of specific assignments.
As a final word of advice, "what is the least amount of work I need to do in order to get the grade I want?" is a much worse way to approach higher education than "how can I learn the most from this class and from my teachers?".
If you want help with computing, please bring a laptop.
| Monday | 6:00--7:00 | Mr. Richardson | Porter Hall 117 |
| Tuesday | 4:00--5:00 | Prof. Shalizi | Wean Hall 4625 |
| Tuesday | 6:00--7:00 | Mr. Richardson | Porter Hall 117 |
| Wednesday | 11:00--12:00 | Prof. Shalizi | Doherty Hall 1211 |
| Wednesday | 6:00--7:00 | Mr. Richardson | Porter Hall 117 |
If you cannot make the regular office hours, or have concerns you'd rather discuss privately, please e-mail me 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 the next-to-last class, 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.
Everything you turn in for a grade must be your own work, or a clearly
acknowledged borrowing from an approved source; this includes all mathematical
derivations, computer code and output, figures, and text. Any use of permitted
sources must be clearly acknowledged in your work, with citations letting the
reader verify your source. You are free to consult the textbook and
recommended class texts, lecture slides and demos, any resources provided
through the class website, solutions provided to this semester's
previous assignments in this course, books and papers in the library, or online
resources, though again, all use of these sources must be acknowledged in your
work.
In general, you are free to discuss homework with other students in the class, though not to share work; such conversations must be acknowledged in your assignments. You may not discuss the content of assignments with anyone other than current students or the instructors until after the assignments are due. (Exceptions may be made, with prior permission, for approved tutors.) You are, naturally, free to complain, in general terms, about any aspect of the course, to whomever you like.
During the take-home exams, you are not allowed to discuss the content of the exams with anyone other than the instructors; in particular, you may not discuss the content of the exam with other students in the course.
Any use of solutions provided for any assignment in this course in previous years is strictly prohibited, both for homework and for exams. This prohibition applies even to students who are re-taking the course. Do not copy the old solutions (in whole or in part), do not "consult" them, do not read them, do not ask your friend who took the course last year if they "happen to remember" or "can give you a hint". Doing any of these things, or anything like these things, is cheating, it is easily detected cheating, and those who thought they could get away with it in the past have failed the course.
If you are unsure about what is or is not appropriate, please ask me before submitting anything; there will be no penalty for asking. If you do violate these policies but then think better of it, it is your responsibility to tell me as soon as possible to discuss how your mis-deeds might be rectified. Otherwise, violations of any sort will lead to severe, formal disciplinary action, under the terms of the university's policy on academic integrity.
On the first day of class, every student will receive a written copy of the university's policy on academic integrity, a written copy of these course policies, and a "homework 0" on the content of these policies. This assignment will not factor into your grade, but you must complete it before you can get any credit for any other assignment.
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.
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.
There is a separate page of resources for learning R.
Current revision of the complete textbook
*: 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 to push its wares that way, but it doesn't.) ^