Stata Course

Stata is statistical software for data science and an alternative to packages like SPSS, R or SAS. The buzzwords: "Obtain and manipulate data. Explore. Visualize. Model. Make inferences.”

The course is“open” and participants can attend those parts of the course that are of most interest. The upside of this is that there is no fee, no attendance sheets and no exam! The downside is that the course will not give any credits in the Ph.d. program.

The courses will be held on Zoom. If you are interested in joining, please register by clicking on the session link for each of the sessions you plan to attend.

You can find course material (Presentations, Syntax and Data) at the end of this page.

Teachers: Hein Stigum, Jonathan Wörn. 

You can sign up for later sessions at any time before the session.

Register for Individual Sessions – Click on Session Link 

The course will have lectures in 3 levels:

• Beginner: No previous experience in Stata.
• Elementary: General knowledge of using Stata (as in the two beginners courses)
• Advanced: Experience in Stata use (as given by the elementary courses)    

In addition, some experience in data handling and statistical analysis will make understanding easier. We are targeting Ph.d. candidates, Post Doctors and Researchers in Medical Statistics and Epidemiology in general.

Teaching

The course will have 3 hours of lectures (12:30-15:30) for each theme. No laptops needed. We will give out exercises with solution syntaxes at the end of each lesson. Participants are encouraged to solve these on their own or in groups.

 

Date	Level	Theme /Session Link	Teacher	Venue
16.feb.	Beginner	Introduction to STATA: Interface, file types, data handeling, basic commands	Jonathan Wörn	Zoom
23.feb.	Beginner	Graphics: Making plots for data and results	Jonathan Wörn	Zoom
2.mars	Elementary	Linear Regression: Standard model, non-linear effects, interactions, effects of outliers, predictions	Hein Stigum	Zoom
9.mars	Elementary	Logistic regression: Standard model, non-linear effects, interaction, effects of outliers, predictions	Hein Stigum	Zoom
23.mars	Advanced	Survival analysis: Flexible Parametric Survival Models	Hein Stigum	Zoom
30.mars	Advanced	Automating analysis: Returned results, macros, matrices, loops	Hein Stigum	Zoom
6.april	Advanced	Programing: Simulating data for Linear, Logistic and Survival data: Writing programs for Simulation, Bootstrapping and Power Calculations	Hein Stigum	Zoom
13.april	Advanced	Individual fixed effects regression: Examining within-unit changes, controlling for unit-specific characteristics. Setting up data; model specification and interpretaton; graphing results.	Jonathan Wörn	Zoom

Organizers

Department of Community Medicine and Global Health.

Contact: Hein Stigum

Presentations

• 1 Stata Introduction (PowerPoint)
• 1 Stata Introduction (syntax)
• 1-demo-file-web.do
• Birth1 (Datafil)
• 2 Graphics (PowerPoint)
• 2 Graphics (Syntax)
• 2-demo-file.do
• Gestational Age and Birth weight (Datafile)
• 3 Linear Regression (PowerPoint)
• 3 Linear Regression (syntax)
• 3 Linear Regression HS-talk
• 4 Logistic Regression (syntax)
• 4 Logistic Regression (PowerPoint)
• 4 Logistic Regression HS talk
• 5 Flexible Parametric Survival Models (PowerPoint)
• 5.0 Program Downloads (syntax)
• 5.0 Cox Model (syntax)
• 5.1 Flexible Parametric Survival Models (syntax)
• 5 Flexible Parametric Survival, HS talk
• Melanoma (data), Kidney (data), Breast Cancer (data)
• 6 Macro, Matrix and Loop, Agenda (PowerPoint)
• 6 Macro, Matrix and Loop, Examples (syntax)
• 6 Macro, Matrix and Loop, Commands (syntax)
• 6 Macro, Matrix and Loop, HS talk
• 6 Gestational Age and Birth Weight
• Tables (Excel)
• 7 Simulating Data and Writing Programs, (PowerPoint)
• 7 Simulating Data and Writing Programs, Examples (syntax)
• 7 Simulating Data and Writing Programs, HS talk
• 7 Flexible Parametric Survival, Simulation
• 7 Sparse Data Bias (Excel)
• 7 Sparse Data Bias (PowerPoint)
• 8-fixed-effects-regression
• 8-fixed-effects-regression_demo_web
• 8-fixed-effects-regression_example
• 8-fixed-effects-regression_example_web
• fe1.dta
• fe2.dta