PyStatsV1 Workbook
Getting started
- Quickstart (all platforms)
- Windows 11 setup (students)
- What you need
- 1) Install Python (3.10+)
- 2) Install Git Bash
- 3) Optional: Install make
- 4) Create a workbook folder + virtual environment
- 5) Initialize and run the Workbook
- Keeping PyStatsV1 up to date
- If something goes wrong
- Understanding the setup commands (Windows + Git Bash)
- 1) Create a virtual environment
- 2) Activate the virtual environment
- 3) Upgrade pip (inside the environment)
- 4) Install PyStatsV1 + Workbook dependencies
- 5) Check your environment
- 6) Create your workbook starter folder
- Chapter 11 note (Windows + Git Bash): missing helper file + PYTHONPATH
- How to do the Study Habits Case Study Pack (TA instructions)
- Quick checklist (use this for every chapter)
- Where did my outputs go?
- Study Habits case study: what to write down
- “My Own Data” mini-guide (TA instructions)
- Try “My Own Data” with Notepad (copy/paste example + expected results)
- Workflow: Run → Inspect → Check
- Study Habits Case Study Pack
- Case Study Pack: Intro Stats
- My Own Data: a mini-guide
- Troubleshooting
- Track C: Psychology / Education
- Track D Student Edition (Workbook Landing)
- Big picture
- Phase 0 — On-ramp (see the data)
- Phase 1 — Accounting foundations (what the numbers mean)
- Phase 2 — Data preparation (make the dataset analysis-ready)
- Phase 3 — Describe performance + report responsibly
- Phase 4 — Statistics for decisions (business lens)
- Phase 5 — Forecasting + governance
- How to apply Track D to your own data
- Track D Playbook: Big Picture
- Track D chapter index (PyPI)
- D00PEEKDATA — peek at the (canonical) datasets.
- D00SETUPDATA — (re)generate the synthetic datasets.
- D01 — Accounting as a measurement system.
- D02 — Double-entry and the general ledger as a database.
- D03 — Financial statements as summary statistics.
- D04 — Assets: inventory, fixed assets, depreciation (and leases, conceptual).
- D05 — Liabilities, payroll, taxes, and equity: obligations and structure.
- D06 — Reconciliations as quality control.
- D07 — Preparing accounting data for analysis.
- D08 — Descriptive statistics for financial performance.
- D09 — Plotting/reporting style contract + example outputs.
- D10 — Probability and Risk in Business Terms.
- D11 — Sampling and Estimation (Audit and Controls Lens).
- D12 — Hypothesis Testing for Decisions (Practical, Not Math-Heavy).
- D13 — Correlation, Causation, and Controlled Comparisons (NSO running case).
- D14 — Regression Driver Analysis (NSO).
- D15 — Forecasting foundations (NSO).
- D16 — Seasonality and baseline forecasts (NSO).
- D17 — Revenue forecasting via segmentation + drivers (NSO v1).
- D18 — Expense forecasting (NSO running case).
- D19 — Cash flow forecasting (direct method, 13-week).
- D20 — Integrated forecasting (P&L + balance sheet + cash tie-out).
- D21 — scenario planning, sensitivity, and stress testing.
- D22 — Financial statement analysis toolkit.
- D23 — Communicating results (memos, dashboards, governance).
- Track D Workbook: Business Statistics for Accounting Data
- Track D Dataset Map
- Track D Outputs Guide
- Track D: Apply what you learned to your data
- Track D BYOD: Bring Your Own Data
- Track D assignments: labs + rubric (TA)
- Track D Lab 0/1 (PyPI-only)
- 1. Learning goals
- 2. Lab structure
- 3. Environment setup talk track
- 4. Initialize the Track D workbook
- 5. List the available Track D runs
- 6. Run
d00_peek_data(data tour) - 7. Run
d01(Chapter 1 checks + key metrics) - 8. Outputs (what to open)
- 9. Run the smoke tests (
business_smoke) - 10. Common issues and quick fixes
- 11. Discussion prompts (if time)
- 12. Closing script (30 seconds)
- Track D Lab + TA Notes (PyPI-only)
- Appendix A: Command block (TA slide)