Kalil Warren

StatTeacher Toolkit v1.0.0

About the App

The StatTeacher Toolkit is an interactive statistics problem generator built for instructors and students. Configure population parameters, generate synthetic datasets with controlled properties, and compute complete statistical results — including test statistics, critical values, effect sizes, and confidence intervals — all directly in your browser. No server required.

Select a statistical test, adjust the parameters, and click Generate Problem to produce a practice dataset with a full results table ready for lecture, homework, or exams. Results can be downloaded as a CSV file.

After generating a problem, a Student Problem Generator card appears automatically. Narrative tests (Z-test, t-tests, Pearson) produce a plain-English scenario with step-by-step questions and an instructor key. Table-based tests (ANOVA, Repeated-Measures ANOVA, regression) produce a partially blanked summary table at Easy, Moderate, or Hard difficulty. All worksheets and keys can be exported as Excel files.

Python · Pyodide Statistics Education Hypothesis Testing Student Problem Generator Runs in Browser Open-Source

Loading Statistical Engine

Setting up the Python environment in your browser via Pyodide. This takes about 15–30 seconds on first load.

Initializing…

Supported Tests

Z-Test & t-Tests

One-sample Z-test (known σ), one-sample t-test, independent-samples t-test, and repeated-measures t-test. All report Cohen's d, r², standard error, and a 95% confidence interval.

Independent ANOVA

One-way and fully-crossed factorial designs with arbitrary factor combinations. Returns a complete source table with SS, df, MS, F, and p-values for all main effects and interactions.

Repeated-Measures ANOVA

One-way within-subjects design partitioned into Between Treatments, Between Subjects (nuisance), and Error. Reports F = MSBT / MSError with the full source table, decision, and dataset export.

Pearson Correlation

Generates X and Y datasets and computes SSX, SSY, SPXY, Pearson r, and r². Tests r against a null ρ using a t transformation.

Simple Linear Regression

Least-squares regression returning the slope, intercept, fitted equation, and an ANOVA-style F-test with Regression / Residual / Total source table.

Citation

If you use the StatTeacher Toolkit in your course or research, please cite it as:

Warren, K. (). StatTeacher Toolkit [Python/Pyodide web application].