# 140.613.11Data Analysis Workshop I

Location
East Baltimore
Term
Summer Institute
Department
Biostatistics
Credit(s)
2
2023 - 2024
Instruction Method
In-person
Start Date
Monday, June 12, 2023
End Date
Friday, June 16, 2023
Class Time(s)
M, Tu, W, Th, F, 1:30 - 5:00pm
Auditors Allowed
No
No
Course Instructor(s)
Contact Name
Frequency Schedule
Every Year
Resources
Prerequisite

Experience in using a statistical analysis package; 140.611-612; or equivalent experience

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. Learns basic methods of data organization/management and simple methods for data exploration, data editing, and graphical and tabular displays. Includes additional topics: comparison of means and proportions, simple linear regression and correlation.
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 and Quizzes
• 40% Final Project