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Advanced Data Analysis Workshop

June 27-July 1, 2022
1:30 p.m. – 5:00 p.m.
2 credits
Course number: 140.620.11 (in-person)
                                    140.620.49 (synchronous online)


This is a hybrid course with both a synchronous online section (140.620.49) and an in-person section (140.620.11). Please choose the modality you need (either online or in-person) when registering in SIS.


Course Instructor:

  • Patrick Tarwater


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.

Student Evaluation: quizzes and final exam

Learning Objectives:

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

  • Conduct a simple linear, logistic or survival regression and correctly interpret the regression coefficients and their confidence interval

  • Conduct a multiple linear, logistic or survival regression and correctly interpret the coefficients and their confidence intervals

  • Examine residuals and adjusted variable plots for inconsistencies between the regression model and patterns in the data and for outliers and high leverage observations

  • Fit and compare different models to explore the association between outcome and predictor variables in an observational study.


Grading Options: Letter Grade or Pass/Fail

Prequisites: 140.613.11, 140.614.11 or equivalent

Course Materials: No required textbooks; Course materials will be provided in class