Biostat 406 - Spring 2017
- Instructor: Donatello Telesca
- Lecture: TR 4:00 pm to 5:30 pm - PH 41-268
- Office Hours: T 3:00 pm - 4:00 pm / W 11:30 am - 12:30pm [Ph 21-254B]
- Labs: (1A) M 9:00 am PH 61-262, (1B) M 3:00 pm PH 52-279, (1C) M 2:00 pm PH 61-262
- Teaching Assistant: Bingling Wang (binglingwang at ucla dot edu)
Schedule of Lectures
- (4/04) Introduction - course logistic - Statistical modeling and reproducibility (Lect1.Rmd, Lect1.pdf)
- (4/06) Introduction to Linear Models (Lect2.Rmd, Lect2.pdf)
- (4/11) Dummy Variables, Interactions, Variance Stabilization, Bootstrap (Lect3.Rmd, Lect3.pdf)
- (4/13) Model/Variable Selection (Lect4.Rmd, Lect4.pdf)
- (4/18) Statistical Learning (Lect5.Rmd, Lect5.pdf)
- (4/20) Generalized Linear Models - Logistic Regression (Lect6.Rmd, Lect6.pdf)
- (4/25) Binary Data - Logistic Regression and Generalizations (Lect7.Rmd, Lect7.pdf)
- (4/27) Midterm Review (Review.pdf)
- (5/02) Midterm 1
- (5/04) Count Data - Poisson and Quasi-Poisson - NegBinom - Zero Inflation (Lect8.pdf, Lect8.Rmd)
- (5/09) Time to Event Data and Survival Analysis (Lect9.pdf., Lect9.Rmd)
- (5/11) Missing Data (Lect10.pdf, Lect10.Rmd)
- (5/16) Linear Dimension Reduction - Principal Components Analysis (Lect11.pdf, Lect11.Rmd)
- (5/18) Latent Variables - Factor Analysis (Lect12.pdf, Lect12.Rmd)
- (5/23) Midterm Review
- (5/25) Midterm 2
- (5/30) Discriminant Analysis (Notes.pdf)
- (6/01) Clustering (Notes.Rmd, Notes.pdf)
- (6/06) Multilevel Models (Notes.Rmd, Notes.pdf)
- (6/08) Multilevel Models
Course syllabus (.pdf)
|4/5 HW assignments||20%||Due on Thursdays|
|Midterm 1||25%||May 02 (In class)|
|Midterm 2||25%||May 25 (In class)|
|Final Project||30%||Due on or before June 14|
All assignments are to be produced in markdown with knitr and submitted via CCLE turnitin.
Afifi, A., May, S. and Clark, VA. Practical Multivariate Analysis (Fifth Edition). CRC Press.
James G., Witten, D., Hastie, T. and Tibshirani, R. An Introduction to Statistical Learning with Applications in R. Springer. (*e-book from the library)
Dalgaard P. Introductory Statistics with R (Second Edition). Springer. (*e-book from the library).
Computing and Software
Biostatistics 406 will be taught using the R computational framework, which is a powerful and freely available tool for computation, analytics and graphics.
To make the most of your first lab please follow the following steps:
- Install R-studio on your machine. You simply need the open-source desktop version downloadable here.
- The course will focus on reproducible research through dynamic/smart documentation. Please get somewhat familiar with R Markdown as a platform for document creation here.