Biostat 406 - Spring 2019
Instructor: Donatello Telesca (dtelesca at ucla dot edu)
Lecture: TR 4:00pm-5:30pm [PUB HLT 61269]
Labs: 1A [M: 9:00am-9:50am, PUB HLT 61262], 1B [3:00pm -3:50pm, PUB HLT 61269], 1C [2:00pm -2:50pm, PUB HLT 61235]
Office Hours: [Telesca: Thursdays 3:00PM (PH-21-254B), Frankenburg: T 10:00AM (PH- A1279)]
Teaching Assistants: Ian Frankenburg (ian dot frankenburg at ucla dot edu), Bryan Lin (bryllliant at gmail dot com).
All projects and assignments should be e-mailed by the due date to firstname.lastname@example.org. This e-mail must be used exclusively for HW and project submissions, and not as a way to communicate with your instructor or TAs.
Final Project Instructions (Instructions.pdf) [Due 6/10]
Schedule of Lectures
(5/2) Midterm I
(5/23) Classification [Lecture14.pdf, no Rmd file available] [HW 4 due]
(5/28) Review for Midterm II [Slides.pdf]
(5/30) Midterm II
(6/06) Rudiments of Bayesian Inference [Lecture15.pdf]
Class Syllabus [Syllabus.pdf]
(4/01) Assisted set up of R Studio and Markdown tools [Bring your laptop][LAB0.Rmd]
(4/08) Review of Linear Models - Interpretation and R graphics [Lab1.Rmd]
(4/15) Factorial Predictors Model Selection [Lab2.Rmd]
(4/22) Shrinkage and Learning [Lab3.Rmd]
(4/29) Generalized Linear Models [Lab4.Rdm]
(5/6) Count Data [Lab5.Rmd]
(5/13) Missing Data [Lab6.Rmd]
(5/20) PCA and Factor Analysis [Lab7.Rmd]
(6/03) Assistance with Final Project
Body Fat and Weight (fat.dat.txt)
Money and Democracy (CAFE.csv)
Food and Nutrition (food.txt)
Breast Cancer (gbcs.csv)
|5 Take Home assignments||20%||Due date on HW|
|Midterm 1||25%||May 2 (In class)|
|Midterm 2||25%||May 30 (In class)|
|Final Project||30%||Due on or before June 10|
[AMC] Afifi, A., May, S. and Clark, VA. Practical Multivariate Analysis (Fifth Edition). CRC Press.
[JWH] 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. Simple installation instructions are available here. A comprehensive reference is available here.