The all-in-one lab for economics & statistics courses: students upload real data, get the right method, run models in the browser, learn the theory, cite the papers, and export reproducible work — all in one place. Recommend one platform to your whole class.
BUILT FOR ECONOMICS & STATISTICS COURSES AND LABS
No R installs, no Stata license, no "which test do I use?" — just the analysis.
Upload your own file, or pick a built-in World Bank or IMF dataset.
Describe the question; it suggests a method that actually fits your columns.
Real R runs on the server, in the browser. Nothing to install on the laptop.
What the method does, its assumptions, and the mistakes to avoid.
Code, output, tables, citations, and a reproducible bundle for the assignment.
Stop losing the first two weeks of the course to "R won't install." Teach the method, not the software.
Every student works in the same place — no version drift, no environment support tickets to your TA.
Every number a student turns in traces back to the code that produced it. If the data doesn't fit the method, it says so instead of inventing a result.
Each result exports as a bundle — data, script, output, and citations — that you or a TA can re-run and get the same numbers. Grade with confidence.
Students spend their time on the economics — reading the assumptions, interpreting coefficients, catching the common mistakes — instead of debugging package installs. Bring your own datasets and point the whole class at them.
Run it hosted for the class, or entirely on your department's own machine so nothing leaves the building — your call.
No R packages to compile, no Python environment to fix, no Stata or SPSS license, no "not enough RAM." Students open a link and run the model — the heavy lifting happens on the server.
Not sure if it's fixed effects or random effects, a logit or a probit? Describe what you want to find out. The workbench suggests an approach that fits your columns, writes the R, and explains what the method does, its assumptions, and when not to use it.
Students who don't have data don't get stuck. Real, curated macro datasets are built in — World Bank and IMF panels ready to analyse — alongside a curated library of papers tied to each method.
Most AI tools ask you to trust them. This one shows its work: the columns it used, the code it ran, and the real output every figure came from. Unverifiable numbers are withheld, not guessed — the guarantee that makes it safe to grade.
Grouped by what you're trying to find out — the technical names are there, but a student doesn't need them to start.
What drives the outcome? OLS · multiple regression · logit/probit · quantile.
Countries or firms over time. Fixed / random effects · Hausman · clustered SE · difference-in-differences.
Trend, seasonality, forecasts. ARIMA/SARIMA · stationarity (ADF) · decomposition.
Instrumental-variables-style adjustment, survey weights, SEM & mediation, survival, clustering, meta-analysis and more — each writing verified code and, where you need it, matching SPSS / Mplus / Stata-style syntax for your own workflow.
Paste output from your own R, Stata, SPSS, or Mplus and get a plain-language explanation you can trust.
Offline and on-prem when you need it — a trust layer under everything, not a marketing line.
We'll load a dataset, run a real analysis, and show you the answer, the proof, and the reproducible export — the exact flow your students would use.