Browser practice and Stata code
Stata practice
and applied analysis.
The practice workspace shows the observations behind each result. Guided lessons cover data preparation, models and interpretation; the do-file builder turns an analysis plan into Stata code.
No installation needed for practice. Export your work to Stata.
. scatter score hours
Open the practice workspace to explore these data.
Learning paths
Foundations through advanced analysis
The beginner path explains the workspace and the data before introducing models. If you already know the basics, enter at the question you need to work through.
Commands, samples and data changes
Inspect observations, handle missing values and create variables with deliberate checks.
Graphs, regression and uncertainty
Connect data to a fitted line. Read coefficients, examine residuals and compare models.
Samples, group summaries and reproducible data work
Inspect what contributes to a group statistic, change the level of observation and recover the original data.
Merge and reshape records
3 investigations with 17 checked tasks. Trace each match, preserve repeated measurements and examine conflicting values.
Groups, sample accounting and functional forms
Compare groups, distinguish estimation from prediction, and interpret transformed and interacted variables.
Data checks and a complete analysis
Investigate quality problems, work with a second dataset and prepare a reproducible Stata project.
Intermediate and advanced investigations
Instrumental-variable estimation and inference.
After regression, examine what an instrument identifies and what changes when its assumptions fail.
Predicted values, coefficients and standard errors
Follow an observation through the first stage, calculate the IV ratio and examine the residual used for inference.
Weak instruments and unbounded confidence sets
Weaken the first stage, test individual candidates and interpret disconnected or uninformative confidence sets.
A strong first stage can still identify the wrong quantity
Keep instrument strength fixed while breaking exclusion. Examine why a larger sample does not repair the design.
From output to understanding
The command is only
part of the work.
A table of coefficients can be correct and still be misread. The workspace pairs each exercise with an interpretation check, and makes the sample, units and assumptions visible.
Work through missing values →. count if hours > 5 12 . count if hours > 5 & !missing(hours) 11
Numeric missing values compare greater than ordinary numbers in Stata. One extra observation enters the first calculation. The second command makes the intended sample explicit.
Beyond browser practice
Continue with the full Stata workflow.
The browser implements a clearly described subset. These materials support work in an installed copy of Stata.
Command reference & scripts
Import, cleaning, merges, reshaping, models and reporting.
↗02Applied lessons & projects
Longer workflows with downloadable datasets and do-files.
↗03Advanced econometrics
Model assumptions, panel data, endogeneity and diagnostic tools.
↗04Statistical concepts
Definitions, interpretations and the conditions that matter.
↗About STATAverse
A teaching project by Dr. Ian Helfrich.
Dr. Helfrich is an economist, researcher and tutor with a Ph.D. from Georgia Tech. STATAverse connects the practical work of using Stata with careful statistical interpretation.