Course Website Locator: id271-01

Harvard School of Public Health

The following course websites match your request:

Spring 1 2010

Departments of Environmental Health and Epidemiology
Dr. J. Schwartz
2.5 credits
Lectures and seminars. Two 2-hour sessions each week.

This course covers applied advanced regression analysis. Its focus is on relaxing classical assumptions in regression analysis to better match what epidemiological data really looks like. Specifically, the course will cover nonlinear exposure-response relationships and repeated measure designs, including non-parametric and semi-parametric smoothing techniques, generalized additive models, and time series models. In addition to the theoretical material, students will apply these techniques using R to actual datasets including modeling the effects of environmental exposures on health outcomes. These techniques also are widely applicable to problems in infectious disease, psychiatric, nutritional, occupational, and cancer epidemiology.
Course Activities: Lectures and structured workshops in the instructional computer facility.
Course Note: Basic biostatistics and a course in regression analysis recommended; Minimum enrollment of 10 students. (5.06)

Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Spring 1 2009

Departments of Environmental Health and Epidemiology
Dr. J. Schwartz
2.5 credits
Lectures and seminars. Two 2-hour sessions each week.

This course covers applied advanced regression analysis. Its focus is on relaxing classical assumptions in regression analysis to better match what epidemiological data really looks like. Specifically, the course will cover nonlinear exposure-response relationships and repeated measure designs, including non-parametric and semi-parametric smoothing techniques, generalized additive models, and time series models. In addition to the theoretical material, students will apply these techniques using R to actual datasets including modeling the effects of environmental exposures on health outcomes. These techniques also are widely applicable to problems in infectious disease, psychiatric, nutritional, occupational, and cancer epidemiology.
Course Activities: Lectures and structured workshops in the instructional computer facility.
Course Note: Basic biostatistics and a course in regression analysis recommended; Minimum enrollment of 10 students. (5.06)

Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Spring 1 2008

Departments of Environmental Health and Epidemiology
Dr. J. Schwartz
2.5 credits
Lectures and seminars. Two 2-hour sessions each week.

This course covers applied advanced regression analysis. Its focus is on relaxing classical assumptions in regression analysis to better match what epidemiological data really looks like. Specifically, the course will cover nonlinear exposure-response relationships and repeated measure designs, including non-parametric and semi-parametric smoothing techniques, generalized additive models, and time series models. In addition to the theoretical material, students will apply these techniques using R to actual datasets including modeling the effects of environmental exposures on health outcomes. These techniques also are widely applicable to problems in infectious disease, psychiatric, nutritional, occupational, and cancer epidemiology.
Course Activities: Lectures and structured workshops in the instructional computer facility.
Course Note: Basic biostatistics and a course in regression analysis recommended; Minimum enrollment of 10 students. (5.06)

Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Spring 1 2007

Departments of Environmental Health and Epidemiology
Dr. J. Schwartz
2.5 credits
Lectures and seminars. Two 2-hour sessions each week.

This course covers applied advanced regression analysis. Its focus is on relaxias classical assumptions in regression analysis to better match what epidemiological data really looks like. Specifically, the course will cover nonlinear exposure-response relationships and repeated measure designs, including non-parametric and semi-parametric smoothing techniques, generalized additive models, and time series models. In addition to the theoretical material, students will apply these techniques using R to actual datasets including modeling the effects of environmental exposures on health outcomes. These techniques also are widely applicable to problems in infectious disease, psychiatric, nutritional, occupational, and cancer epidemiology.
Course Activities: Lectures and structured workshops in the instructional computer facility.
Course Note: Basic biostatistics and a course in regression analysis recommended; Minimum enrollment of 10 students. (5.06)

Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Copyright © 2012 The President and Fellows of Harvard College