# telmo-correa/all-of-statistics

Self-study on Larry Wasserman's "All of Statistics"

Repository: https://github.com/telmo-correa/all-of-statistics
Canonical: https://ross.abutalabs.com/products/all-of-statistics
Language: Jupyter Notebook
License Family: other
Topics: statistics, solutions, self-study, probability
Last push: 2022-12-11T11:18:48+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2375, "days_push": 1361, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1249, forks 326 (observed 2026-08-28T04:04:07.907756+00:00)

## What it is
A collection of Jupyter notebooks with notes and complete exercise solutions for self-study of Larry Wasserman's 'All of Statistics: A Concise Course in Statistical Inference'. Each chapter combines LaTeX-formatted definitions and theorems with executable Python.

## Use cases
- self-study statistics from All of Statistics
- check my solutions to Wasserman's exercises
- learn probability and statistical inference with Python notebooks
- find worked examples of statistical inference problems
- supplement a statistics course with chapter notes

## When to choose
- you are working through All of Statistics and want notes or solution references
- you prefer learning statistical inference via executable Jupyter notebooks

## When to avoid
- you need a textbook replacement or full tutorial course
- you need the latest edition's exercises exactly matched
- you need production statistical software or a library

## Facets
- artifact type: learning-resource
- maturity: stable
- function: data-science, math
- domain: education, tutorials
- platform: python
- tags: jupyter-notebooks, statistics, probability, self-study, solutions-manual, wasserman

## Member repositories
- telmo-correa/all-of-statistics (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:07.907756+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T05:07:49.134960+00:00, confidence not recorded.
  - readme: https://github.com/telmo-correa/all-of-statistics (fetched 2026-08-28T04:04:07.907756+00:00, sha 152e845c4a9f)
- Data as of 2026-08-30T08:39:29.467469+00:00.