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Course Catalog

140.620.20 Advanced Data Analysis Workshop

Department:
Biostatistics
Term:
1st term
Credits:
2 credits
Academic Year:
2019 - 2020
Location:
East Baltimore
Dates:
Thu 10/03/2019 - Fri 10/04/2019
Class Times:
  • Th F,  8:30am - 5:20pm
Auditors Allowed:
No
Grading Restriction:
Letter Grade or Pass/Fail
Contact:
Judith Holzer
Course Instructor:
Resources:
Prerequisite:

Data Analysis Workshops I and II (140.613 and 140.614)

Description:

Covers methods for the organization, management, exploration, and statistical inference from data derived from multivariable regression models, including linear, logistic, Poisson and Cox regression models. Students apply these concepts to two or three public health data sets in a computer laboratory setting using STATA statistical software. Topics covered include generalized linear models, product-limit (Kaplan-Meier) estimation, Cox proportional hazards model.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Conduct a simple linear, logistic or survival regression and correctly interpret the regression coefficients and their confidence interval
  2. Conduct a multiple linear, logistic or survival regression and correctly interpret the coefficients and their confidence intervals
  3. Examine residuals and adjusted variable plots for inconsistencies between the regression model and patterns in the data and for outliers and high leverage observations
  4. Fit and compare different models to explore the association between outcome and predictor variables in an observational study
Methods of Assessment:

laboratory exercises (20%); an exam (30%) and independent data analysis project (50%)

Enrollment Restriction:

Part-time DrPH students in the Tsinghua cohort only

Instructor Consent:

Consent required for all students

Consent Note:
For consent, contact:

judith.holzer@jhu.edu

Special Comments:

This course will be offered over a 2-day period in Baltimore. Students may be required to complete assignments prior to the start of class.