Lab Session: Th 9:05 - 9:55 in Ford Hall B53
Yuhong Yang, 376 Ford Hall
Email: yyang@stat.umn.edu, Phone: 612-626-8337
MWF 10:05-11:00
Yingwen Dong, 495 Ford Hall
Email: ywdong@stat.umn.edu, phone: 4-5569
Office hour: T, Th 10:00 - 11:00
S. Weisberg (2005), Applied Linear Regression (3rd Ed), Wiley.
J. Faraway (2006) Extending the Linear Model with R, Chapman & Hall/CRC.
This course focuses on applied regression. It will cover linear regression with one and many predictors; graphics; model building and assessment; diagnostics; generalized linear models, including logistic and Poisson regressions; two way and higher dimensional contingency tables.
Two midterm exams: 45%; Homework: 15%; Final Exam: 40%
Homework will be assigned on a weekly basis and it is due on Thursdays in lab sessions. No late homework will be accepted without official excuse. Only part of the assigned problems will be graded, but solutions for all the problems will be provided. It is fine for students to work together on homework problems, but write-up of the solutions must be done independently.
Students are expected to attend all the lectures and arrive on time.
We will be using software R for this course. The main web site for R is www.r-project.org, where one can find basics of R, including an introduction to R and program download sites.
Exam 1, Friday, October 20, 2006
Exam 2, Monday, November 20, 2006
Final Exam, 10:30am-12:30pm, Saturday, December 16
University and department policy is that ``I'' grades are used only when there is a small amount of unfinished work that the student can complete on his or her own before the end of the following semester, when there was a legitimate excuse why the work could not be done on time, and when arrangements have been made with the instructor as to when the work will be done. ``I'' grades are not given when there is a large amount of work undone and the student would need to attend the class in the next semester to learn the material.
This publication/material, and all other handouts in Statistics 8061, is available in alternative formats upon request. Please contact Mary, School of Statistics, 313 Ford, 625-7300.
Academic integrity is essential to a positive teaching and learning environment. All students enrolled in University courses are expected to complete coursework responsibilities with fairness and honesty. Failure to do so by seeking unfair advantage over others or misrepresenting someone else's work as your own, can result in disciplinary action. The University Student Conduct Code defines scholastic dishonesty as follows: Scholastic Dishonesty: Scholastic dishonesty means plagiarizing; cheating on assignments or examinations; engaging in unauthorized collaboration on academic work; taking, acquiring, or using test materials without faculty permission; submitting false or incomplete records of academic achievement; acting alone or in cooperation with another to falsify records or to obtain dishonestly grades, honors, awards, or professional endorsement; altering forging , or misusing a University academic record; or fabricating or falsifying data, research procedures, or data analysis. Within this course, a student responsible for scholastic dishonesty can be assigned a penalty up to and including an "F" or "N" for the course. If you have any questions regarding the expectations for a specific assignment or exam, please ask.
Students will frequently receive email from the instructor regarding homework assignment (hints, suggestions, etc.) and announcements.