SDS 322 Biostatistics

Instructor: Likai Chen (likai.chen[at]wustl.edu)

Lectures: 2:00-2:50 p.m on Mondays, Wednesdays, and Fridays in Cupples II/230

Office hour:

Schedule

Topics covered

This is a second course in applied statistics with examples from biology and medicine. Topics include Bayes rule, Markov chains, maximum likelihood estimation with MCMC, classical statistical inference, ANOVA and MANOVA, multivariate visualization, multiple regression, correlation, and classification.

Prerequisites

The prerequisite is Math 3200 or equivalent mathematical maturity and experience.

Textbook

Statistics Using R with Biological Examples
Kim Seefeld and Ernst Linder,
an e-text that you may download freely.
If you desire a paper copy, you may have it printed and bound at any copy shop from this PDF file.

Fundamentals of Biostatistics
Bernard Rosner

Exams

There will be one midterm on 03/05 (Wed) and one final on 05/02 4:00-6:00 p.m. No reference material or electronic devices will be allowed. One page note (letter size and two-sided) may be brought to exams.

Make-up exams are strongly discouraged. If you are aware of a conflict, please inform the instructor before the exam. Make-up exam will only be given if (1) within 1 week of the standard exam and (2) suitable documentation is provided within 2 days.

Homeworks

There will be bi-weekly homework assignments. About 8 homework problems (may including computer homework problems) will be assigned each time and the solutions to all problems will be posted on the course website. Most of the exam and final questions will be similar to these. Hence you are strongly suggested to compare your answers with the posted solution. Weekly homework is due on Wednesday midnight. NO LATE HOMEWORK WILL BE ACCEPTED.

Grades

Your grade will be based on weekly homework, midterm and final exam.

Midterm 30%
Final 50%
Homework 20%

The threshold for each grade

A B C
90 or above 75-90 60-75
Only very top students will get A+. A- is for students whose grades are very close to A, B+ is for students whose grades close to A-, etc.

Software

Open-source software R for statistical computing, and its manual .
Download R from Wustl's software.
Download R Studio from its developer's website.