📦 Resources¶
The original course material this repository was built from — a full-semester introductory statistics course (15 weeks) with a textbook, weekly slides, graded assignments, discussions, and a three-part business project.
Note
The source files — the textbook PDF, weekly slide decks, assignment workbooks, and graded coursework — are kept local only. An inner .gitignore excludes *.pdf, *.doc(x), *.ppt(x), *.xls(x), and *.txt from the repository, so none of it appears on GitHub or the published site. This page is the section's public landing.
What Lives Here (Locally)¶
| Folder | Contents |
|---|---|
Book/ |
Introductory Statistics (textbook PDF) and the Try-It answer key |
Week 1/ … Week 15/ |
Per-week reading extract, lecture slides, and assignment workbooks |
Discussion/ |
Four discussion assignments: graphs, correlation, the normal distribution, null and alternative hypotheses |
Midpoint Assesment/ |
Midpoint study guide and assessment workbook |
Project/ |
The three-part business statistics project (parts 1, 2, 3) with data workbooks, plus the criminal-justice and health variants of the same brief |
Reflection Essay/ |
End-of-course reflection |
How the Weekly Material Maps to This Course¶
The notes and exercises here reorganize that week-by-week material into a topic-based reference, so related ideas sit together instead of being split across whichever week they happened to fall in.
| Source weeks | Chapter in this repo |
|---|---|
| Week 1 | 01 · Foundations of Data |
| Weeks 2–3 | 02 · Frequency Distributions |
| Weeks 3–4 | 03 · Central Tendency · 04 · Variation & Position |
| Weeks 5–6 | 05 · Probability |
| Weeks 6–7 | 06 · Discrete Distributions |
| Weeks 7–8 | 07 · Continuous & Normal |
| Week 9 | 08 · Sampling Theory & CLT |
| Weeks 10–11 | 09 · Confidence Intervals |
| Weeks 11–12 | 10 · Hypothesis Testing |
| Weeks 12–13 | 11 · t-Tests |
| Weeks 13–14 | 12 · Chi-Square & ANOVA |
| Weeks 14–15 | 13 · Correlation & Regression |
The graded business project (parts 1–3) is the model for the capstone project, which runs the same describe → estimate → test → model → report pipeline on a fresh dataset.
Probability gets fuller treatment here than the source weeks gave it: the source material moves quickly from data description to distributions, so chapters 05-01 through 05-03 were written from scratch to cover sample spaces, the addition and multiplication rules, conditional probability, Bayes' theorem, and the counting rules properly before the distributions arrive.
Recommended External Resources¶
Free textbooks¶
| Resource | Why |
|---|---|
| OpenStax — Introductory Statistics 2e | The standard free text; matches this course's sequence closely |
| OpenIntro Statistics | Free PDF, excellent on inference and study design; strong exercise sets |
| Modern Dive | Statistics through R, with tidyverse throughout |
| Think Stats | The Python-first take, computation-led rather than formula-led |
Reference and tables¶
| Resource | Why |
|---|---|
| NIST/SEMATECH e-Handbook | The authoritative free reference for every method in this course |
| Statistical tables (z, t, χ², F) | For when you need the printed-table route |
| R documentation | Every function's arguments and defaults |
| SciPy stats reference | The Python equivalent |
| Microsoft — statistical functions | Excel's full statistical function list |
Visual and interactive¶
| Resource | Why |
|---|---|
| Seeing Theory | Animated probability and inference — the best CLT visualization anywhere |
| Rossman/Chance applets | Sampling distributions, confidence intervals, randomization tests |
| Spurious Correlations | A permanent reminder that correlation is not causation |
Practice¶
| Resource | Why |
|---|---|
| Khan Academy — Statistics & Probability | Video walkthroughs with practice sets |
| Kaggle Datasets | Real data to redo the projects on |
| UCI Machine Learning Repository | Classic, well-documented datasets |
Companion Courses¶
This repository sits under Data Science & AI on ijk37.com, alongside:
- Data Analytics — Excel — the spreadsheet skills this course assumes
- Python — Data Analytics — NumPy, Matplotlib, and Pandas
- Machine Learning — where regression from Chapter 13 goes next
Tip
Statistics is the prerequisite for machine learning, not a detour around it. Every concept in Chapter 13 — least squares, residuals, R², overfitting, train/test thinking — is the foundation the ML course builds on.