.. _business-track:

Track D – Business Statistics & Forecasting for Accountants
===========================================================

Track D teaches **business statistics and forecasting** to learners who work with
accounting-shaped data (bookkeeping, AP/AR, payroll, staff accounting, finance ops).

The core idea is simple:

**Don’t just calculate results — engineer them.**
We treat statistical analysis like **production software**:

* reproducible inputs and deterministic outputs,
* automated checks (tests) so you can trust what you share,
* “audit-friendly” artifacts (tables, figures, memos) that support decisions.

Run Track D as a Workbook (PyPI-only)
-------------------------------------

If you're a student following Track D, the easiest path is the **Track D Workbook**.
It installs from PyPI and generates a self-contained folder (scripts + tests + data).

.. code-block:: bash

   python -m venv .venv
   # Windows (Git Bash)
   source .venv/Scripts/activate
   python -m pip install --upgrade pip
   python -m pip install "pystatsv1[workbook]"

   pystatsv1 workbook init --track d --dest track_d_workbook
   cd track_d_workbook

   # Start here
   pystatsv1 workbook run d00_peek_data
   pystatsv1 workbook run d01

   # Optional: run the included smoke test
   pystatsv1 workbook check business_smoke

The workbook ships canonical datasets (seed=123) under ``data/synthetic/`` and writes
chapter outputs to ``outputs/track_d/``. See :doc:`workbook/track_d` for the dataset map,
output conventions, and tips.

The running case and data
-------------------------

Track D uses a **synthetic, accounting-shaped dataset family** generated by the
PyStatsV1 simulator scripts (sometimes referred to as **LedgerLab**).

There are two dataset “sizes” used across Track D:

* **LedgerLab core datasets (Chapters 1–3):**
  small ledgers used to teach double-entry, the GL, and statements as summaries.
  You will see folders like ``data/synthetic/ledgerlab_ch01``.

* **North Shore Outfitters (NSO v1) running case (Chapters 4+):**
  a richer, realistic small business scenario used for operational + financial analysis,
  written to ``data/synthetic/nso_v1``.

Everything is generated locally, so you can **modify assumptions** and rerun the book.

Reproducibility quick start
---------------------------

Inputs and outputs follow two conventions:

* **Inputs live in** ``data/synthetic/...`` (generated datasets)
* **Outputs live in** ``outputs/track_d/`` (chapter artifacts)

To (re)generate the NSO v1 dataset:

.. code-block:: bash

   make business-nso-sim
   make business-validate

To run an individual chapter (example: Chapter 15):

.. code-block:: bash

   make business-ch15

Chapter map (high level)
------------------------

Track D is designed as a practical progression:

* **Ch 1–3:** accounting as measurement; double-entry; statements as summary statistics
* **Ch 4–6:** accounting subsystems + controls (inventory, payroll/taxes, reconciliations)
* **Ch 7–9:** make data analysis-ready; descriptive stats; reporting style contract
* **Ch 10–13:** probability, sampling, hypothesis tests, controlled comparisons
* **Ch 14:** regression driver analysis (plus deep-dive appendices 14A–14E)
* **Ch 15:** forecasting foundations + forecast hygiene (baseline methods + backtesting)

Where to start
--------------

Start with :ref:`business-ch01`.