# 140.613.20Data Analysis Workshop I

## Discontinued

Location:
East Baltimore
Term:
3rd term
Department:
Biostatistics
Credits:
2 credits
2021 - 2022
Instruction Method:
TBD
Auditors Allowed:
No
Course Instructor:
Contact:
Judith Holzer
Resources:
Prerequisite:

Experience in using a statistical analysis package; 140.611-612

Description:

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. In the first workshop (140.613), students learn basic methods of data organization/management and simple methods for data exploration, data editing, and graphical and tabular displays. Additional topics include comparison of means and proportions, simple linear regression and correlation. Enrollment limited: students must have a laptop computer with Stata/IC versions 13.0, 14.0, 15.0, or 16.0 installed.

Learning Objectives:

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

1. Create, save and edit STATA datasets, log files and do files
2. Use STATA to perform exploratory data analysis for continuous and dichotomous variables
3. Use STATA do files to create reproducible analyses
4. Explain the distinction between and appropriate uses of the binomial, Poisson and normal probability models
5. Use STATA to perform paired and unpaired t-tests for differences in group means
6. Describe the appropriate use of paired and unpaired t-tests and the interpretation of the resulting STATA output
7. Use STATA to perform a chi-squared test and compute confidence intervals for differences in group proportions, relative risks and odds ratios
8. Describe the appropriate use of chi-squared tests and the interpretation of the resulting STATA output
9. Use STATA to visualize relationships between two continuous measures
10. Use STATA to fit simple linear regression models, and interpret relevant estimates from the results
Methods of Assessment:

This course is evaluated as follows:

• 60% Lab Assignments
• 40% Final Exam

Enrollment Restriction:

Part-time DrPH students in the Tsinghua cohort only

Instructor Consent:

Consent required for all students

Consent Note:

restricted to students in the Tsinghua DrPH cohort only

For consent, contact: