Lectures TR 1000-1120 GLSN 100
Instructor: Charlotte Wickham, 255 Weniger
Office hours: Mon & Weds 1-2pm Weniger 255
TA: Chris Comiskey firstname.lastname@example.org
The analysis of serially correlated data in both time and frequency domains. Autocorrelation and partial autocorrelation functions, autoregressive integrated moving average models, model building, forecasting; filtering, smoothing, spectral analysis, frequency response studies.
Topics covered will include:
After completing ST565 you will be familiar with the issues in understanding, analyzing and interpreting data measured in time. You will be able to:
40% homework + 20% midterm + 20% project + 20% final
Roughly weekly homeworks may consist of readings, mathematical derivations, simulations and complete data analyses. Homeworks will be made available on the class website and, unless specified otherwise, handed in at the start of class on Tuesday. Late homeworks will not be accepted. Your lowest homework score will be dropped.
Tentatively scheduled for Thrusday of week 6.
Proposal due week 7, final project due week 10… more details closer to the time.
This quarter I am planning to follow quite closely: The Analysis of Time Series: An Introduction, Sixth Edition (Chapman & Hall/CRC Texts in Statistical Science)
This is available to read online through the OSU library. If you want to buy a reference book for time series this is a good one to start with. The only downside is it has very little code for getting things done in R (I will generally provide this in the lecture notes). It is not required that you buy this book, but I do recommend supplementing the lecture material with the readings provided from this book, or use one of the other freely available books below.
For Stats students: there is also a copy available in the Stats Department Library.
Optional Three other books I have found useful and are available online:
Accommodations are collaborative efforts between students, faculty and Disability Access Services (DAS). Students with accommodations approved through DAS are responsible for contacting me prior to or during the first week of the term to discuss accommodations. Students who believe they are eligible for accommodations but who have not yet obtained approval through DAS should contact DAS immediately at (541) 737-4098.
Academic dishonesty is a serious offense and will be addressed following the guidelines set out in the Academic Regulations of OSU (go to http://catalog.oregonstate.edu, click on Registration Information → Academic Regulations, and read AR 15).
The Student Conduct Code http://studentlife.oregonstate.edu/studentconduct/offenses-0 defines Academic dishonesty as
… an act of deception in which a Student seeks to claim credit for the work or effort of another person, or uses unauthorized materials or fabricated information in any academic work or research, either through the Student’s own efforts or the efforts of another.
Examples include, but are not limited to, the following: