201B Syllabus

Stats 201B: Regression Analysis: Model Building, Fitting, and Criticism

Syllabus (Winter 2014)

Homepage: https://ccle.ucla.edu/course/view/14W-STATS201B-1
Instructor: Hongquan Xu <hqxu@stat.ucla.edu> Phone: (310) 206-0035
Lecture Hours: MW 2-3:15pm at BH 9436
Office Hours: MW 1-1:50pm at MS 8955
TA: Safaa Dabagh <sofiadabagh@ucla.edu>, Office Hours: Tuesday 11-12 at MS 8208
Textbooks (required):

J. J. Faraway (2005). "Linear Models with R," Chapman & Hall.
J. J. Faraway (2006). "Extending the Linear Model with R," Chapman & Hall.

Description: Applied regression analysis, with emphasis on general linear model (e.g., multiple regression) and generalized linear model (e.g., logistic regression). Special attention to modern extensions of regression, including regression diagnostics, graphical procedures, and bootstrapping for statistical inference.

Topics:

Linear model: Estimation, Inference, Diagnostics
Problems with the Predictors and the Error, Robust Regression
Transformation, Variable Selection, Shrinkage Methods
Generalized linear models: Logistic regression, Poisson regression

Grading:

Homework (20%): No late Homework!
Midterm Exam (25%): Monday, February 10, 2014.
Final Project (15%): Data analysis (TBD).
Final Exam (40%): Friday, March 21, 2014, 9-11am (2 hours).

Statistical Software: We will use R, a free software environment for statistical computing and graphics. You can download R and get information from the R Home Page (http://www.r-project.org/) or from UCLA (http://cran.stat.ucla.edu). R commands used in the textbooks are available from Julian Faraway's website. You can be download RStudio from (http://www.rstudio.com).

Academic Integrity: As a student and member of the University community, you are here to get an education and are, therefore, expected to demonstrate integrity in your academic endeavors. All students must uphold University of California Standards of Student Conduct as administered by the Office of the Dean of Students. Students are subject to disciplinary action for several types of misconduct, including but not limited to: cheating, multiple submissions, plagiarism, prohibited collaboration, facilitating academic dishonesty, or knowingly furnishing false information. You may have assignments or projects in which you work with a partner or with a group. For example, you are welcome, and even encouraged, to work with others to solve homework problems. Even though you are working together, the assignment you submit for a grade must be IN YOUR OWN WORDS, unless you receive specific instructions to the contrary. For more information about academic integrity, please go to www.deanofstudents.ucla.edu.