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Course Catalog


Department: Biostatistics
Term: 4th term
Credits: 4 credits
Contact: Frank Curriero
Academic Year: 2012 - 2013
Course Instructor:

Introduces statistical techniques used to model, analyze, and interpret public health related spatial data. Analysis of spatially dependent data is cast into a general framework based on regression methodology. Topics covered include the geostatistical techniques of kriging and variogram analysis and point process methods for spatial case control and area-level analysis. Although the focus is on statistical modeling, students will also cover topics related to clustering and cluster detection of disease events. Although helpful, knowledge of specific GIS software is not required. Instruction in the public domain statistical package R, (to be used for analysis), is provided.

Learning Objective(s):
Upon successfully completing this course, students will be able to:
describe the concept of spatial dependence and apply techniques to quantify it with different types of spatial data
conduct routine spatial statistical analysis using extended regression techniques within the R Statistical Computing Environment software
identify the potential consequences of overlooking spatial information when conducting certain types of public health research

Methods of Assessment: Method of student evaluation based on assignments and exam
Location: East Baltimore
Class Times:
  • Tuesday 1:30 - 2:50
  • Thursday 1:30 - 2:50
Lab Times:
  • Wednesday 4:30 - 5:20
Enrollment Minimum: 10
Instructor Consent: No consent required

140.621.-624 or 140.651-.654

Auditors Allowed: Yes, with instructor consent
Grading Restriction: Letter Grade or Pass/Fail
Jointly Offered With:
Special Comments: The course schedule includes 2 lecture periods and one lab per week. The lab hour is devoted mostly to computing for the assigned problem sets.