This course will present a set of scientific questions relevant to climate change science and impact studies, from the perspective of statistical data analysis. We are going to delve into this very dynamic and prominent research topic, with huge policy implications as well as complex and exciting data-driven problems, where statistics has started to contribute significantly, but more involvement of statisticians in a truly inter-disciplinary mode would certainly be beneficial, and likely spark interesting methodological development. However, the course will strive to be not overly methodological/technical so to engage and reward a multi-disciplinary body of students. For more details see the course overview
The objectives of this course are to learn about some important areas of climate change research with a strong component of statistical data analysis, in order to familiarize students with some of the scientific questions, the language and the data sources, stimulate the application of rigorous statistical methods to these problems and possibly inspire new statistical methodology.
Mon/Tue/Thu 11:00AM-12:15PM Sequoia Hall Room 200
Claudia Tebaldi, Sequoia Hall, room 136 (Prof. Switzer's office). ctebaldi@climatecentral.org. Office hours: Tue 3:30-5:00, or by appointment.
Paul Pong ckpong@stanford.edu
Familiarity with regression methods, multivariate analysis, the Bayesian paradigm, and the statistical software R.
The course encourages students to use R. R implements the S language, which is a programming language very useful for statistical applications. R is avaliable for free from CRAN, and runs on all platforms. There are many introductory documents on the CRAN website (click on the Contributed button under Documentation). The first in the list by John Maindonald is recommended.
Students are encouraged to use the Emacs editor, since there exists a wonderful ESS mode for Emacs, which understands R syntax and more. Online documentation for ESS is available. (Emacs is also terrific for editing latex documents, so the effort pays dividends).
There will be homework assignments ranging from simple analysis of datasets to more science-oriented questions. Brief informal presentations by the students will also be planned during the course.
The grade will be determined by homework and class participation.
There is no textbook for the course, we will use articles from the recent literature and some of the IPCC assessment report material available from the IPCC website http://www.ipcc.ch/ipccreports/index.htm.
Please consult the course web page regularly. The URL is http://www.stanford.edu/class/stats300/. The Supplements section is updated regularly to provide useful material relating to the course.