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During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

Unit of study_

STAT2911: Probability and Statistical Models (Adv)

This unit is essentially an advanced version of STAT2011, with an emphasis on the mathematical techniques used to manipulate random variables and probability models. Common distributions including the Poisson, normal, beta and gamma families as well as the bivariate normal are introduced. Moment generating functions and convolution methods are used to understand the behaviour of sums of random variables. The method of moments and maximum likelihood techniques for fitting statistical distributions to data will be explored. The notions of conditional expectation and prediction will be covered as will be distributions related to the normal: chi^2, t and F. The unit has weekly computer classes where you will learn to use a statistical computing package to perform simulations and carry out computer intensive estimation techniques like the bootstrap method.

Code STAT2911
Academic unit Mathematics and Statistics Academic Operations
Credit points 6
Prerequisites:
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(MATH1X21 or MATH1931 or MATH1X01 or MATH1906 or MATH1011) and a mark of 65 or greater in (DATA1X01 or MATH10X5 or MATH1905 or STAT1021 or ECMT1010 or BUSS1020)
Corequisites:
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None
Prohibitions:
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STAT2011

At the completion of this unit, you should be able to:

  • LO1. construct appropriate statistical models involving random variables for a range of modelling scenarios. Compute (or approximate with a computer if necessary) numerical characteristics of random variables in these models such as probabilities, expectations and variances
  • LO2. fit such models in outcome 1. to data (as appropriate) by estimating any unknown parameters
  • LO3. compute appropriate (both theoretically and computationally derived) measures of uncertainty for any parameter estimates
  • LO4. assess the goodness of fit (as appropriate) of a fitted model
  • LO5. apply certain mathematical results (e.g. inequalities, limiting results) to problems relating to statistical estimation theory
  • LO6. prove certain mathematical results (e.g. inequalities, limiting results) used in the course.

Unit outlines

Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.