140.614.13
Data Analysis Workshop II
Course Status
Cancelled
Course Status
Cancelled
Location
East Baltimore
Term
Winter Institute
Department
Biostatistics
Credit(s)
2
Academic Year
2024 - 2025
Instruction Method
Synchronous Online
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
Yes
Grading Restriction
Letter Grade or Pass/Fail
Contact Name
Frequency Schedule
Every Year
Resources
Prerequisite
140.613
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.
Learning Objectives
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
This course is evaluated as follows:
- 80% Lab Assignments
- 20% Final Exam
Instructor consent required for registrants not concurrently enrolled in a JHSPH part-time degree program