Getting Started with PyStatsV1

Version: v0.1

Release date: 2026-06-10

Publisher: NEKpress.ca

What PyStatsV1 is

PyStatsV1 is an MIT-licensed applied statistics learning project. It emphasizes plain Python scripts, reproducible workflows, synthetic data for teaching, command-line execution, generated summaries, and tests.

This resource is a practical quick start. It is not a complete statistics textbook, not statistical consulting, and not professional advice.

Who it is for

This resource is for students, instructors, tutors, researchers, analysts, and careful beginners who want an inspectable Python path for applied statistics practice.

Fastest start

Install the student workbook bundle:

python -m pip install -U pip
python -m pip install "pystatsv1[workbook]"
pystatsv1 doctor
pystatsv1 workbook init
cd pystatsv1_workbook
python scripts/psych_ch10_problem_set.py
pytest -q

Full source path

To inspect the full repository:

git clone https://github.com/pystatsv1/PyStatsV1.git
cd PyStatsV1
python -m venv .venv
source .venv/bin/activate
python -m pip install -U pip
pip install -e .
pip install -r requirements-dev.txt
make lint
make test

First learning loop

  1. Create or clone the project.
  2. Run a doctor or environment check.
  3. Run one chapter script.
  4. Inspect the generated summary.
  5. Run the matching test.
  6. Change one small input only after you understand the baseline.
  7. Save evidence, not secrets.

Chapter orientation

The PyStatsV1 repository describes chapter-style workflows. Implemented or documented examples include psychology and applied statistics topics such as one-sample tests, confidence intervals, independent and paired t-tests, ANOVA, regression, diagnostics, transformations, collinearity, and model building.

The project also includes business-statistics documentation and examples for measurement, statements as summaries, descriptive statistics, probability, sampling, hypothesis testing, regression driver analysis, forecasting, scenario planning, and communicating results.

Why reproducible statistics matters

A statistical claim should not depend on memory or a screenshot. A reproducible workflow records the data path, code path, command, output, and test. That makes it easier to teach, debug, explain, and improve the analysis.

Contribution note

The software is MIT licensed. The resource is available free. Voluntary contributions through LearnToProgram.ca support continued PyStatsV1 development, examples, documentation, and learning materials. Contributions are not required to use the software.

Disclaimer

This resource is educational. It is not statistical consulting, legal advice, tax advice, medical advice, financial advice, privacy advice, cybersecurity advice, or payment-compliance advice.