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).


Assignments

All projects and assignments should be e-mailed by the due date to biostat406@gmail.com. This e-mail must be used exclusively for HW and project submissions, and not as a way to communicate with your instructor or TAs.

Submission Instructions and Document Example: [HWexample.pdf, HWexample.Rmd]


Final Project Instructions (Instructions.pdf) [Due 6/10]


Schedule of Lectures

Class Syllabus [Syllabus.pdf]


Labs

  • (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]

  • (5/27) Holiday

  • (6/03)


Data


Coursework

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

Reading List

  1. Required

    [AMC] Afifi, A., May, S. and Clark, VA. Practical Multivariate Analysis (Fifth Edition). CRC Press.

  2. Recommended

    [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)

  3. Recommended

    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:

  1. Install R-studio on your machine. You simply need the open-source desktop version downloadable here.

  2. 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.