Data Analysis Workshop II
June 20-24, 2022
1:30 p.m. – 5:00 p.m.
Course Number: 140.614.49 (synchronous online)
140.614.11 (in person)
This is a hybrid course with both a synchronous online section (140.614.49) and an in-person section (140.614.11). Please choose the modality you need (either online or in-person) when registering in SIS.
"Dr. McGready is really passionate about the subject matter and really excellent at explaining the concepts and answering any questions that arise."—Student, 2021
"Dr. McGready is one of the most outstanding professors I have had in my entire educational career, spanning from elementary school to medical and graduate school. He is engaging, interactive, passionate and devoted to teaching. He presents the material in enlightening ways that stimulate further learning and understanding."—Student, 2019
Intended for students with a broad understanding of biostatistical concepts used in public health sciences who seek to develop additional data analysis skills. Emphasizes concepts and illustration of concepts applying a variety of analytic techniques to public health datasets in a computer laboratory using Stata statistical software. Masters advanced methods of data analysis including analysis of variance, analysis of covariance, nonparametric methods for comparing groups, multiple linear regression, logistic regression, log-linear regression, and survival analysis.
Student Evaluation: Student evaluation based on laboratory exercises, an exam, and completion of an independent data analysis project.
Upon successfully completing this course, students will be able to:
- Use STATA to visualize relationships between two continuous measures
- Use STATA to fit simple linear regression models, and interpret relevant estimates from the results
- Use STATA to fit multiple linear regression models to relate a continuous outcome to multiple predictors in one model and to help assess confounding, interaction, and goodness-of-fit
- Interpret the relevant estimates from multiple linear regression
- Use STATA to graph lowess smoothing functions to relate the probability of a dichotomous outcome to a continuous predictor
- Use STATA to fit multiple logistic regression models to relate a dichotomous outcome to multiple predictors in one model and to help assess confounding, interaction, and goodness-of-fit
- Setup cohort study data into STATA survival analysis format
- Use STATA to graph Kaplan-Meier curves and perform log-rank tests
- Use STATA to fit Cox regression models to relate time-to-event data to multiple predictors in one model and to help assess confounding, interaction, and goodness-of-fit
- Interpret the confounding estimates from Cox regression
Methods of Assessment:
1) Lab Assignments and Quizzes 60%
2) Final Project 40%
Prerequisite: 140.611 and 140.612 or equivalent
Grading Options: Letter Grade or Pass/Fail
Course Materials: Students must have a laptop computer with Intercooled Stata 16 or Intercooled 15 installed. Student discounts are available for Intercooled Stata.