Skip to content
Notes for ETC2420 Monash University
HTML JavaScript CSS Other
Branch: website
Clone or download

Latest commit

Fetching latest commit…
Cannot retrieve the latest commit at this time.

Files

Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
content
layouts/partials
static
themes/hugo-xmin
.gitignore
LICENSE
README.md
Statistical_Thinking.Rproj
config.toml

README.md

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)

You can’t perform that action at this time.