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STAT-320-001 – Biostatistics

Department of Mathematics and Statistics

M/TH 9:45am-11:00 am in Don Myers 121

Instructor: Hugo Van Dyke

Office: Don Myers Technology and Innovation Building (DMTI), Room 208K

Email: [email protected]

Prerequisite: STAT-202/203 or equivalent

Office hours: MTH: 11:30am – 1:30pm or by appointment (this could be done via zoom)

https://american.zoom.us/j/93363991481

Meeting: M and TH: 9:45-11:00 AM Myers 121

Zoom link for additional Office Hours : https://american.zoom.us/j/93363991481

Required Text

Rosner, B. (2011) Fundamentals of Biostatistics, Eighth Edition. Brooks/Cole, Boston, MA, USA. (the ebook is included with purchase of access to webassign)

We will cover portions of chapters 1 through 12

Course Content and Learning Objectives

STAT-320 is an introduction to the statistical methodology commonly used in public health, medical, and biological studies. This course emphasizes working with data and communicating statistical ideas. A breadth of topics will be covered including: basic study design, tests of significance, confidence intervals, t-procedures, chi-square and Fisher’s exact test, linear regression, analysis of variance, nonparametric methods, and more advanced topics as time permits. This section will use the R computer program to conduct analyses and occasionally Statcrunch to use built-in applets.

The major focus for this course is the ideas behind and methods for drawing conclusions about a population from a sample. At the end of this course you will be expected to identify the major concepts related to statistical reasoning and to statistical inferences for drawing such conclusions, recognize how these concepts are used in disciplines related to health and medicine, and implement the methods yourself in statistical analyses using the methods covered. In particular, you are expected to be able to (1) identify the appropriate statistical model or models for a given analysis, (2) write the model in the correct notation, (3) implement the model in a software package on a given set of data, (4) interpret the output in the context of the study, (5) diagnose model deficiences, (6) suggest improvements to the model if necessary, and (7) summarize the results of the analysis.

Grading

Homework (15% webassign, 25% “written” assignments) 40%

Exam 1: 20%

Exam 2: 20%

Final Exam (or project): 20%

Homework is assigned every one to two weeks and consists two parts: an online part via webassign and written part submitted via Canvas. You may discuss homework problems with the instructor and other students in the class but all work turned in should be your own and reflect your understanding of the material. Direct copying of assignments or solutions from any source will not be tolerated. Read the Academic Integrity section below. 

Notes about Webassign homework: Webassign is far from perfect. Occasionally the software grades questions incorrectly or has an issue with how you have rounded your answer. Parts of questions will even include material that we have not covered in class (or involve intense calculation by hand). These parts while providing a challenge will not be counted toward your webassign final grade. (Taking these considerations into mind, aim for 80% on the webassign assignments)

Exams may be take-home, in-class, or a combination. Any take-home portion is not to be discussed with anyone, except the instructor. Please read the Academic Integrity section below. A project may be assigned that will have deadlines throughout the semester.

Academic Integrity

I am required to report cases of academic dishonesty to the dean of the College of Arts and Sciences. Please read American University’s Academic Integrity Code carefully and ask me if you have questions. See the AU Academic Integrity website for additional information.

Canvas

I use Canvas for dissemination of course material and information. I will post slides and handouts before the class meets. I highly recommend looking through the material in advance and after we meet.

Grades on individual written assignments are posted in Canvas but I maintain the official grade record in my office. I do not use the Canvas to compute your final grade in the course. The weights are different and all assignments might not be posted. You can attempt to compute your course average at any time by using the weights given on the previous page, but if you have a question about your grade in the course then please stop by my office hours or email me.  

Computing and Software

We will use the R software program to complete assignments, exams, and the project.

It is a free, open source software package. Go to the R website (http://cran.r-project.org/) to download the main package. You should consider using RStudio – an enterprise level “front end” which makes R a little more user friendly. RStudio Desktop is free to download and install. You still need R installed on your computer to use RStudio.

Statcrunch is available by going statcrunch.american.edu and typing in your AU username and password.

Data sets for homeworks assignments and class examples from the textbook are available on the book’s companion website. I will try to post the data in canvas in at .CSV format for easiest loading into R.

Mathematics and Statistics Tutoring Lab and Software Support

The Department of Mathematics and Statistics Tutoring lab in DMTI 103 provides free tutoring for statistics courses up to the 300-level. No appointment is needed to see a tutor. This may be online. Support for Statistical Software is provided in the Research Commons Area in the Library.

Signing Up for WebAssign:

Go to: https://www.webassign.net/

Web Assign Class Key: american 6391 0296