This course aims to
provide epidemiologists and clinical researchers with a firm grounding in the
foundations of probability and statistical theory. The course emphasizes conceptual
understanding rather than a “black box” approach or rigorous
mathematical proofs. Students will be
exposed to software to do statistical analysis on data sets but will not
receive formal instruction on data management.
Specific topics
that will be discussed include: random
variables, expectation, variance, probability distributions, the Central Limit
Theorem, sampling theory, hypothesis testing, confidence intervals;
correlation, regression, analysis of variance, nonparametric tests, and an
introduction to least squares and maximum likelihood estimation.
There will be an
emphasis on analysis of biomedical data.
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Professor |
Teaching Assistant(s) |
Raymond R. Balise, Ph.D. balise at stanford |
Lamiya Sheikh lamiyas at stanford |
A comfortable knowledge of Windows XP/Vista, Mac OS or UNIX.
Monday and Wednesday 4:15-5:45 in M108B in the medical school. The classroom is by the Café in medical school (close to Lane library and below the computer lab) and the map is here:
http://lane.stanford.edu/graphics/maps/learningspaces_map.pdf
By appointment in Redwood Building T213D. Directions can be found here: www.stanford.edu/~balise/FindBalise.htm
If you would like to ask a question or help others, please visit the course newsgroup which is named: su.class.hrp259. While not truly required for the class, you will suffer if you don’t have access to the news. If you do not know how to subscribe to a newsgroup and you use Windows http://www.stanford.edu/services/email/config/thunderbird/newsreader/pc/ or a Mac http://www.stanford.edu/services/email/config/thunderbird/newsreader/mac/. Screenshots of my setup can be found here: www.stanford.edu/class/hrp223/2007/newsgroup.ppt
Biostatistics: A Methodology for the Health Sciences (2nd Edition) by Gerald van Belle, Lloyd Fisher, Patrick J. Heagerty, Thomas Lumley http://www.amazon.com/Biostatistics-Methodology-Sciences-Probability-Statistics/dp/0471031852/ref=pd_bbs_sr_1/102-7869045-8398536?ie=UTF8&s=books&qid=1190400480&sr=8-1
The Little SAS Book for Enterprise Guide 4.1 at SAS: http://www.sas.com/apps/pubscat/bookdetails.jsp?catid=1&pc=61054
SAS for Dummies http://www.amazon.com/SAS-Dummies-Computer-Tech/dp/0471788325/ref=pd_bbs_sr_1/102-7869045-8398536?ie=UTF8&s=books&qid=1190400573&sr=1-1
Assignments and Grading:
Class Participation 10%
Homework 30%
Take-Home Midterm 20%
Take-Home Final Exam 40%
Grading Policy:
1. If I grade on a curve, the class mean will be set at a
B+. I generally do not give A+’s.
2. For students needing to use the pass/fail option (e.g., medical students), you will need to achieve a grade of B or higher to pass this course.
Homework:
A short problem set will be due at the beginning of most class sessions. Problem sets will be graded as:
ˇ 2 points = excellent (completed, mostly correct)
ˇ 1.5 points = satisfactory (completed, missing some concepts)
ˇ 1 point = incomplete (not finished or poor effort, but made some attempt)
ˇ 0 points = not handed in
You must not violate the computer virus policy below.
Any student that sends me a software virus (or any other malicious code) will fail the course. There will be no exceptions made. Therefore, you are strongly advised to download the latest version of the Symantec AntiVirus definitions and check your files prior to sending me any email. If you need virus protection check here http://www.stanford.edu/services/ess/ and you can download the software for free. If you have any questions about how to update your virus definitions, ask!
Each of the assignments will be due at the beginning of class on the day specified. Assignments will be downgraded 50% each 24 hours the assignment is late.
Many of the assignments will require you to work with a statistical analysis package. You can choose your tools but I will be showing examples using SAS/Enterprise Guide. I can provide good support for SAS on Windows, and some support for R or S-Plus.
PowerPoint slides are here.
van Belle chapters 1 and 2
Assignment 1 is here.
PowerPoint slides are here.
van Belle Chapter 3 especially 3.3.2 through Note 3.7
Assignment 2 is here.
PowerPoint slides are here.
Sample datasets and EG projects are here.
PowerPoint slides are here.
van Belle Chapter 4 especially 4.1-4.2 4.4-4.5
PowerPoint slides are here.
van Belle Chapter 4 and 5 especially 4.6 5.8.
Sample datasets and EG projects are here.
Assignment 3 is here and is due before class starts on October 10th.
PowerPoint slides are here.
Sample datasets and EG projects for alpha and beta discussion are here.
Sample R code for alpha and beta discussion is here.
Sample datasets and EG projects for t-tests are here.
PowerPoint slides are here.
van Belle Chapter 8 (especially 8.1-8.6, 8.9), 10 (but don’t panic over the sigma notation).
EG project to do the primary analysis in van Belle Chapter 10 is here.
EG project to do nonparametric analysis is here.
EG project to do One-way ANOVA is here.
The midterm is here and will be due on 10/28.
PowerPoint slides are here.
van Belle Chapter 10
EG project to do Two-way ANOVA is here.
Generalized Anxiety Disorder data is here.
Memory data is here.
PowerPoint slides are here.
PowerPoint slides are here.
PowerPoint slides will be here.
Mortality Enterprise Guide project is here. (Edited for Nov5)
Mortality Excel file is here.
van Belle Chapter 9.
PowerPoint slides are here.
van Belle Chapter 9 and 11.
PowerPoint slides are here.
Polynomial Enterprise Guide project is here.
PowerPoint slides are here.
PowerPoint slides are here.
PowerPoint slides are here.
PowerPoint slides are here.
Code for binomial data is here.
Code for heart disease data is here.
Code for AZT and cat scratch favor data is here.
Sensitivity, Specificity, PVP, PVN is here.
A bunch of the code from Fleiss’ categorical analysis book can be found here.
Van Belle Chapter 6.1-6.4 and Chapter 7.1-7.4
PowerPoint slides are here.
Van Belle Chapter 6.1-6.4 and Chapter 7.1-7.4
PowerPoint slides are here. These are essentially the slides from the first lecture so you may want to save a tree and not print them.
The final exam is here
with a dataset for problem 1 (here) and is
due Dec 11 before 10 PM.