PyStatsV1

Workbook – Student Labs (recommended starting point)

  • PyStatsV1 Workbook

Track A – Applied Statistics with Python (Regression)

  • Getting started
  • Introduction: How to study applied statistics with Python and R
  • Chapter 2 – Getting started with R (for Python-first learners)
  • Applied Statistics with Python – Chapter 3
  • Applied Statistics with Python – Chapter 4
  • Applied Statistics with Python – Chapter 5
  • 5.1 Probability in Python (and R)
  • 5.2 Hypothesis tests in Python
  • 5.3 Simulation in Python
  • 5.4 What you should take away
  • Applied Statistics with Python – Chapter 6
  • 6.1 Beginner tutorials and references
  • 6.2 Intermediate references
  • 6.3 Advanced references
  • 6.4 Cross-language comparisons
  • 6.5 IDEs, notebooks, and literate programming
  • 6.6 How PyStatsV1 fits into this ecosystem
  • Applied Statistics with Python – Chapter 7
  • Applied Statistics with Python – Chapter 8
  • Applied Statistics with Python – Chapter 9
  • Applied Statistics with Python – Chapter 10
  • Applied Statistics with Python – Chapter 11
  • Applied Statistics with Python – Chapter 12
  • Applied Statistics with Python – Chapter 13
  • Applied Statistics with Python – Chapter 14
  • Applied Statistics with Python – Chapter 15
  • Applied Statistics with Python – Chapter 16
  • Applied Statistics with Python – Chapter 17
  • Applied Statistics with Python – Chapter 18
  • Chapters overview
  • Teaching guide
  • Contributing

Track B – Psychological Science & Statistics (Psych track)

  • Psychological Science & Statistics – From Inquiry to Insight
  • Psychological Science & Statistics – Chapter 1
  • Psychological Science & Statistics – Chapter 2
  • Psychological Science & Statistics – Chapter 3
  • Psychological Science & Statistics – Chapter 4
  • Psychological Science & Statistics – Chapter 5
  • Psychological Science & Statistics – Chapter 6
  • Psychological Science & Statistics – Chapter 7
  • Psychological Science & Statistics – Chapter 8
  • Psychological Science & Statistics – Chapter 9
  • Psychological Science & Statistics – Chapter 10
  • Chapter 11 – Within-Subjects Designs and the Paired-Samples t-Test
  • Chapter 12 – One-Way Analysis of Variance (ANOVA)
  • Chapter 13 – Factorial Designs and the Two-Way ANOVA
  • Chapter 14 – Repeated-Measures ANOVA
  • Chapter 14 Appendix – Pingouin for Repeated-Measures and Mixed ANOVA
  • Chapter 15 – Correlation
  • Chapter 15 Appendix – Pingouin for Correlation and Partial Correlation
  • Chapter 16 – Linear Regression
  • Chapter 16a Appendix: Linear Regression with Pingouin
  • Chapter 16b – Regression Diagnostics with Pingouin
  • Chapter 17 – Mixed-Model Designs
  • Chapter 18 – Analysis of Covariance (ANCOVA)
  • Chapter 19 – Non-Parametric Statistics
  • Chapter 19a – Rank-Based Non-Parametric Alternatives
  • Chapter 20 – The Responsible Researcher (Conclusion)

Track C – Problem Sets & Worked Solutions (Psych track)

  • Track C – Problem Sets & Worked Solutions
  • Chapter 10 Problem Set – Independent-Samples \(t\) Test
  • Chapter 11 Problem Set – Paired-Samples t Test
  • Chapter 12 Problem Set – One-Way ANOVA
  • Track C – Chapter 13: Factorial Designs (Two-Way ANOVA)
  • Track C — Chapter 14 Problem Set (Repeated-Measures ANOVA)
  • Track C — Chapter 15 Problem Set (Correlation)
  • Track C — Chapter 16 Problem Set: Linear Regression
  • Track C — Chapter 17 Problem Set (Mixed-Model Designs)
  • Track C – Chapter 18 Problem Set (ANCOVA)
  • Track C – Chapter 19 Problem Set (Non-Parametric Statistics)
  • Track C – Chapter 20 Problem Set (Responsible Researcher)

Track D – Business Statistics & Forecasting for Accountants

  • Track D – Business Statistics & Forecasting for Accountants
  • Ch 01 — Accounting as a measurement system
  • Ch 02 — Double-entry and the general ledger as a database
  • Ch 03 — Financial statements as summary statistics
  • Business Chapter 4: Assets — Inventory and Fixed Assets
  • Business Chapter 5: Liabilities — Payroll, Taxes, Debt, and Equity
  • Business Chapter 6: Reconciliations as Quality Control
  • Business Chapter 7: Preparing accounting data for analysis
  • Chapter 8 – Descriptive Statistics for Financial Performance
  • Appendix 8A: Chapter 8 milestone and the big picture (Ch01–Ch08)
  • Track D — Chapter 9: Visualization and reporting that doesn’t mislead
  • Track D — Chapter 10: Probability and risk in business terms
  • Business Chapter 11 — Sampling and Estimation (Audit and Controls Lens)
  • Chapter 12 — Hypothesis Testing for Decisions
  • Chapter 13 — Correlation, Causation, and Controlled Comparisons
  • Track D — Chapter 14
  • Appendix 14A: Chapter 14 milestone — Track D, the NSO system, and our synthetic datasets
  • Appendix 14B: NSO v1 data dictionary cheat sheet (table → grain → keys → joins → checks)
  • Appendix 14C: Chapter 14 artifact dictionary (what each output is for)
  • Appendix 14D: Artifact QA checklist (big picture — what and why before you share results)
  • Appendix 14E: Applying Track D through Chapter 14 to your own real-world data
  • Track D — Chapter 15
  • Track D — Chapter 16
  • Track D — Chapter 17
  • Track D — Chapter 18
  • Track D — Chapter 19
  • Track D — Chapter 20
  • Business Statistics & Forecasting for Accountants (Track D)
  • Business Statistics & Forecasting for Accountants (Track D)
  • Chapter 23 — Communicating results: decision memos, dashboards, and governance
  • Capstone — North Shore Outfitters: Close → Clean → Explain → Forecast → Decide
  • Capstone templates
  • Capstone rubric (100 points)
  • Appendix — Accounting refresher map (from the PDF)
  • Appendix — Track D authoring rules
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