{"id":16275797,"url":"https://github.com/dicook/statistical_thinking","last_synced_at":"2026-02-07T03:01:14.583Z","repository":{"id":68383687,"uuid":"60826006","full_name":"dicook/Statistical_Thinking","owner":"dicook","description":"Notes for ETC2420 Monash University: Note that web site is broken now","archived":false,"fork":false,"pushed_at":"2018-03-05T19:46:21.000Z","size":161052,"stargazers_count":23,"open_issues_count":0,"forks_count":11,"subscribers_count":9,"default_branch":"website","last_synced_at":"2025-07-23T02:46:56.560Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dicook.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2016-06-10T05:28:09.000Z","updated_at":"2025-06-27T19:56:26.000Z","dependencies_parsed_at":"2023-05-22T09:45:24.070Z","dependency_job_id":null,"html_url":"https://github.com/dicook/Statistical_Thinking","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/dicook/Statistical_Thinking","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dicook%2FStatistical_Thinking","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dicook%2FStatistical_Thinking/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dicook%2FStatistical_Thinking/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dicook%2FStatistical_Thinking/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dicook","download_url":"https://codeload.github.com/dicook/Statistical_Thinking/tar.gz/refs/heads/website","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dicook%2FStatistical_Thinking/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29185108,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-07T00:44:15.062Z","status":"online","status_checked_at":"2026-02-07T02:00:07.217Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-10T18:36:26.164Z","updated_at":"2026-02-07T03:01:09.575Z","avatar_url":"https://github.com/dicook.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Statistical Thinking\n\nThis repository is housing material for the course ETC2420/ETC5242 Statistical  Thinking using Randomisation and Simulation taught in Spring 2017 in the Department of Econometrics and Business Statistics at Monash University.\n\n# Tentative outline\n\n* Topic 1: Simulation of games for decision strategies (2 weeks):\n\n  - Week 1, Class 1.  Case studies in randomization using Australian election. What is randomness? (Include the draw vs flip coin tosses)\n  - Week 1, Class 2.  Case studies in randomization (Ch 2, Diez, Barr, Cetinkaya-Rundel). Hypothesis testing I.\n\n  - Lab 1: Introduction to R, functions, permutation, random number generation\n\n  - Week 2, Class 1.  Case studies in randomization (Ch 2, Diez, Barr, Cetinkaya-Rundel). Hypothesis testing II.\n  - Week 2, Class 2.  Decision theory. Computing probabilities of outcomes. Zero-sum two-person: adding reward and loss, saddle points, domination. Criteria for making decisions: minimax, Bayes.\n\n  - Lab 2: Simulate Monty Hall in R\n\nVocabulary: permutation, association, hypothesis, p-value, pseudo-random number generator, simulation, event, probability, zero-sum two-person game, saddle point, domination, minimax, Bayes criterion\n\n* Topic 2: Statistical distributions for decision theory (1.5 weeks)\n\n  - Week 3, Class 1:  Random numbers\n       Mapping random numbers to events for simulation\n       Statistical distributions\n        READING: CT6, Section 1.3-1.9\n\n  - Lab 3: Hypothesis testing using permutation\n\n  - Week 3, Class 2:  Random variables\n      Central limit theorem\n      Density functions\n      Quantiles\n      Estimation\n      Goodness of fit\n\n   - Week 4, Class 1: \n\n   - Lab 4: Finding the best distribution to model olympic medals, estimate number of medals Australia will earn?\n \n* Topic 3: Linear models for credibility theory (1.5 weeks) (Linear models)\n\n - review of regression\n - weighted regression\n - resampling methods for assessing parameter estimates: bootstrap\n - repeated measures, mixed effects models\n\n* Topic 4: Compiling data to problem solve (2 weeks)\n\n - types of data: sampling, survey, observational, experimental\n - working with temporal data, dates, times, seasonality, covariates\n - longitudinal data\n - working with maps and spatial data: chloropleth, point processes\n\nVocabulary: Data, information; population, sample; case, subject, sample, variable, feature; quantitative, integer, real-valued, categorical, ordinal, temporal, spatial, \n  \n* Topic 5: Bayesian statistical thinking (1.5 weeks) -  Charpentier Ch 3\n\n  (i) Introduction to Bayesian methods \n  (ii) Conjugate priors, small sample examples\n  (iii) MCMC\n  (iv) Bayesian regression, and credibility\n\n* Topic 6: Temporal data and time series models (1.5 weeks)\n\n  - Modeling time, autocorrelation, cross-correlation\n  - Prospective life tables (Charpentier Ch 8)\n\n* Topic 7: Modeling risk and loss, with data and using randomization to assess uncertainty (2 weeks)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdicook%2Fstatistical_thinking","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdicook%2Fstatistical_thinking","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdicook%2Fstatistical_thinking/lists"}