• Syllabus

• TA: Pengyuan Wang. Office hours: Tuesday, 9-11am
  Instructor's office hours: Monday, 4:30 after class, in the class room

  
  Take note: This course has only one goal -- prepare 1st year statistics Ph.D. students for research.
  This is not an applied statistics course.

General honor code: You may discuss the problems with each other in general terms, but you must write your own solution. All sources, including friends and colleagues, must be cited. It is important to get used to a stringent code of conduct in scientific writing. On the other hand, use commonsense and attribute where honesty requires it. Two points worth special mention:
*** If you received an extension for a homework, do not consult posted solutions.
*** A grave offense would be consulting solutions of homeworks from previous years.

• Homework 1 (solutions): R practice
  Use the Introduction to R and any other source you like (except solutions posted in previous years).
  Due Fri, Sept 16, 2011, 5pm

• Homework 2 (solutions): linear algebra and latex practice
  Due: Fri, Sept 23, 2010, 5pm
  Edit the LaTex source and submit a PDF file by email.
  If you have never used LaTex, you can first install some free software:
  • MikTex translator,
  • LEd LaTex editor,
  • Manual for Latex.
  In the manual, pay special attention to Section 1.3.2 (special characters) and Chapter 3 for math typesetting (math symbols: Section 3.10). To produce PDF from LaTex, the LEd environment requires you to click the green and blue right arrows in the tool bar. Feel free to check out other free software and other documents. If you find something particularly useful, please, let me know.

• Homework 3 (solutions): Linear algebra and linear models
  Due: Tue, Oct 4, 2011, 5pm
  Edit the LaTex source and submit a PDF file by email.

• Homework 4 (solutions): Numerical linear algebra of Q-R decompositions, R programming
  Due: Fri, Oct 14, 2011, 5pm.

• Writing Homework 1:
  Exercises 4.1 (2.), 4.2 (4.), 4.3 (2. and 8.), 4.4 (8.)
  Send a 'txt' file with your solutions in an attachment to the usual HW email address.
  Due Monday, 2011/10/24, 3pm

• Homework 5 (solutions): Linear Models, and Normal Distributions
  Due: Monday, Nov 7, 2011, 3pm.
  Download and edit the LaTex source.
  

• Homework 6: (data, template, solutions): real estate project.

• Writing Homework 2:
  Rewrite passage 5.2.2, top of p.77.
  Send a 'txt' file with your solutions in an attachment to the usual HW email address.
  Due Monday, 2011/11/21, 10pm

• Homework 7 (solutions): regression, diagnostics, inference, bootstrap, style --- last homework of the semester!
  Due Monday, Dec 12, 5pm. If you need more time, send email to the TA and the instructor.

Unless instructed otherwise, homeworks should be e-mailed in attachments to stat541.at.wharton[at-sign]gmail.com.
The format should be .txt or .pdf or .doc depending on the assignment.
Your checked and graded solutions are returned in e-mail attachments. Search '#PW' (Pengyuan Wang, TA) to find comments.
A score such as 8/10 at the end means '8 out of 10 points'.
A deduction of 2 points does not mean you got two questions wrong; it is only a relative measure of how much below optimal your solutions are.

• Lecture notes -- This is the cumulative and evolving course file (last updated 2011/12/19)

• Introduction to R

• Crib sheet for R

• Readme file for Emacs' ESS macros

• FTSE Data 1991-05-13 through 2006-05-11
• A small book
• Spelling dictionary
• Speech fragment (3.3 mB!)
• Boston housing data with a description of the variables
  Geographical names are in this file.
• Pima Indians diabetes data with a description of the variables
• Laser data with a description of the variables
• Titanic survival data
• Marketing data
• Tips data; analysis
• Detergent data
• Places Rated data with a description
  In a form suitable for xgobi or ggobi: data, column labels, row labels.
  Say 'xgobi places_ggobi' or 'ggobi places_ggobi', depending on which you're using.
• Accounting-Market Rate data (Myers, p.16ff)
• Odd regression data
• Fabric failure data (Myers, p.329f)
• Vaso-restriction data (Myers, p.331f)
• FedEx data, reduced for elasticity demo
• Blocks data, reduced for fixed/variable cost demo
• Algal bloom data; some background is in the README file
• Webserver data
• Wine displayspace data
• Car Models 2003-4
• Body Fat data, variable names, README file,

• Initial tabulation of all variables of a dataframe
• "plot.plus()" and "pairs.plus()": wrappers around 'plot()' and 'pairs()' that jitter categorical variables and provide more space between the points and the borders and axes.
• Empirical two-sample quantile plot with inference
• Mosaic plots, double-decker plots (a competitor for R's mosaic plot function)
• Trimmed correlations, covariances and standard deviations
• Histograms with graphical bootstrap
• Tree-based regression for interpretation and datamining
• Diagnostics plot of linear models with smooth
• Box-Cox transformations
• Collinearity diagnostics with PCA
• Residual diagnostics with null comparisons
• Leverage plots with null comparison
• Utilities for k-means clustering
• Moving blocks bootstrap for time series

IMPORTANT: If a function in the class notes does not work or is not found in your R session, check whether the function is in one of the R code files below. If so, download and read the file into R one more time, even if you thought you had done so earlier. I allow myself to update the code all the time.

• The paper on tree-based regression and classification is in this PDF file.

• The paper on additive principal components is in this PDF file.

• Instructor: Andreas Buja
  Email: stat541.at.wharton[at-sign]gmail.com (urgent: buja.at.wharton[at-sign]gmail.com)
  Office hours: Monday after class 4:30pm, and by appointment.
  Office: JMHH 471
  Class Room: JMHH G86

  TA: Pengyuan Wang
  Email: pengyuan@wharton.upenn.edu
  Office hours: Tuesday 9-11m, and by appointment

• Goal of this course: Prepare statistics Ph.D. students for independent research.
  Accordingly, the demands on conceptual thinking and quick uptake will be considerable as the course progresses.
  What this course is not: not an applied statistic course, not a R course, and not a service course to other departments.
  For a graduate level applied statistic course, see Paul Rosenbaum's 500 level statistical methods.

• Homework will be extensive and approach the level of research problems.
  The weekly homeworks will be the heart of what you retain from this course.

• Grades will be computed from homeworks and class participation alone.
  There will be no midterm or final exams.

• Topics (not necessarily in this order):
  • Linear Models and inference
  • Exploratory data analysis, data visualization
  • Statistical testing exemplified with permutation tests
  • Confidence intervals with bootstrap
  • Tree-based regression
  • ACE and additive models
  • Principal Components
  • Non-parametric curve fitting
  • Bias-variance trade-offs
  • Cross-validation for estimating prediction errors and selecting bandwidths and models
  • Writing for research, including style and clarity, typesetting, web publishing

• Computing: As the tool of choice for the execution of data analyses and simulations, we use the R programming language.
  Note: This is not an R class. R will not even be taught in light of the computational literacy of this year's statistics Ph.D. students.

• Prerequisites:
  • A course in linear algebra:
    basis changes and associated coordinate changes, linear maps, inner product spaces, orthogonal projections, eigen decompositions
  • Probability at the level of Stat 430:
    thinking in random variables, limit theorems
  • Statistical inference at the level of Stat 431:
    statistical tests, confidence intervals, linear models, estimation, sufficiency
  • Programming experience in a high-level language
    Examples of high-level languages: R, Splus, Matlab, Perl, Python, Visual Basic
    Examples of low-level languages: C, C++, Fortran, Java
    Outright detrimental for learning R is exclusive knowledge of SAS due to its very different computational model.

• Undergraduate students contemplating this course: If you do not have a solid background in statistics and linear algebra already as well as some programming experience, you should not take this class or, at a minimum, not rely on credit from it for graduation. As mentioned above, the goal is to prepare students for statistics research, and there will be only one standard of performance for all students.

• Publication quality writing and mathematical typesetting are of utmost importance for statistics research. To get used to the standards of writing research papers in statistics, some homeworks will be required to be typeset in LaTex and submitted by e-mail as a PDF file. You will have to learn LaTex on your own with the help of the TA and other graduate students, but getting started with Latex will be facilitated by templates provided by the instructor, so all you need to do is cannibalize the templates by filling in your solutions.

• The only required texts for the course are the following, one of which not about statistics but about writing:
  • Style --- Lessons in Clarity and Grace by Joseph M. Williams (ISBN 0-321-47935-1).
  • An Introduction to R by Venables and Smith (ISBN 0-9541617-4-2)

• Required web documents:
  • The Science of Scientific Writing
  • An Introduction to R [PDF] [HTML]
    This is the above required book in a free online version. While online is handy for searching, you should still have the book version for actual reading. It costs only about $20.

• If you need more reading about R, look up the numerous books about R or the numerous free web documents about R. Yet another way to find R introductions is to do a search for "Introduction to R". If you find something particularly useful, please, let the instructor know.

• Recommended texts:
  • For regression: Seber and Lee, "Linear Regression Analysis" (Wiley Series in Probability and Statistics)
  • For linear algebra: Strang, "Linear Algebra and its Applications" (Academic Press)
    Strangely, the most fundamental material is no longer in the recent edition: "Linear Transformations, Matrices, and Change of Basis." In older editions this used to be tucked away in the appendix. For this reason, the material is now included in Homework 2: You get to derive it yourself by following instructions.
  • Venables and Ripley, "Modern Applied Statistics with S-Plus" (Springer) a recommended, terse book on a broad array of appl. stats. topics, based on the S/R language.
  • Becker, Chambers, Wilks, "The New S Language" the original S book, also called "the blue book"; even for R users still a good place to start.
  • Chambers, Hastie (eds.) "Statistical Models in S" on the statistical modeling language in S/R, also called "the white book"
  • R. L. Harris, "Information Graphics", an excellent overview of useful and common data visualizations.
    More recent developments can be followed at the TED talks, several by Rosling, another by McCandless.
  As we go along, special topics books will be recommended.

• Supplemental material suggested by previous participants in Stat 541:
  • Justin Rising (Stat 541 '09) points us to a fabulous article on R by Patrick Burns, R Inferno. Print it and keep it on your bedside!
  • Here is a pointer to R blogs.