https://github.com/dicook/statistical_thinking

Notes for ETC2420 Monash University: Note that web site is broken now
https://github.com/dicook/statistical_thinking

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Notes for ETC2420 Monash University: Note that web site is broken now

• Host: GitHub
• URL: https://github.com/dicook/statistical_thinking
• Owner: dicook
• License: cc0-1.0
• Created: 2016-06-10T05:28:09.000Z (about 10 years ago)
• Default Branch: website
• Last Pushed: 2018-03-05T19:46:21.000Z (over 8 years ago)
• Last Synced: 2025-07-23T02:46:56.560Z (11 months ago)
• Language: HTML
• Homepage:
• Size: 154 MB
• Stars: 23
• Watchers: 9
• Forks: 11
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Statistical Thinking

This 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.

# Tentative outline

* Topic 1: Simulation of games for decision strategies (2 weeks):

  - Week 1, Class 1.  Case studies in randomization using Australian election. What is randomness? (Include the draw vs flip coin tosses)

  - Week 1, Class 2.  Case studies in randomization (Ch 2, Diez, Barr, Cetinkaya-Rundel). Hypothesis testing I.

  - Lab 1: Introduction to R, functions, permutation, random number generation

  - Week 2, Class 1.  Case studies in randomization (Ch 2, Diez, Barr, Cetinkaya-Rundel). Hypothesis testing II.

  - 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.

  - Lab 2: Simulate Monty Hall in R

Vocabulary: permutation, association, hypothesis, p-value, pseudo-random number generator, simulation, event, probability, zero-sum two-person game, saddle point, domination, minimax, Bayes criterion

* Topic 2: Statistical distributions for decision theory (1.5 weeks)

  - Week 3, Class 1:  Random numbers

       Mapping random numbers to events for simulation

       Statistical distributions

        READING: CT6, Section 1.3-1.9

  - Lab 3: Hypothesis testing using permutation

  - Week 3, Class 2:  Random variables

      Central limit theorem

      Density functions

      Quantiles

      Estimation

      Goodness of fit

   - Week 4, Class 1: 

   - Lab 4: Finding the best distribution to model olympic medals, estimate number of medals Australia will earn?

 

* Topic 3: Linear models for credibility theory (1.5 weeks) (Linear models)

 - review of regression

 - weighted regression

 - resampling methods for assessing parameter estimates: bootstrap

 - repeated measures, mixed effects models

* Topic 4: Compiling data to problem solve (2 weeks)

 - types of data: sampling, survey, observational, experimental

 - working with temporal data, dates, times, seasonality, covariates

 - longitudinal data

 - working with maps and spatial data: chloropleth, point processes

Vocabulary: Data, information; population, sample; case, subject, sample, variable, feature; quantitative, integer, real-valued, categorical, ordinal, temporal, spatial, 

  

* Topic 5: Bayesian statistical thinking (1.5 weeks) -  Charpentier Ch 3

  (i) Introduction to Bayesian methods 

  (ii) Conjugate priors, small sample examples

  (iii) MCMC

  (iv) Bayesian regression, and credibility

* Topic 6: Temporal data and time series models (1.5 weeks)

  - Modeling time, autocorrelation, cross-correlation

  - Prospective life tables (Charpentier Ch 8)

* Topic 7: Modeling risk and loss, with data and using randomization to assess uncertainty (2 weeks)