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Unit of study_

BSTA5023: Probability and Distribution Theory

2022 unit information

This unit will focus on applying the calculus-based techniques learned in Mathematical Background for Biostatistics (MBB) to the study of probability and statistical distributions. These two units, together with the subsequent Principles of Statistical Inference (PSI) unit, will provide the core prerequisite mathematical statistics background required for the study of later units in the Graduate Diploma or Masters degree. Content: This unit begins with the study of probability, random variables, discrete and continuous distributions, and the use of calculus to obtain expressions for parameters of these distributions such as the mean and variance. Joint distributions for multiple random variables are introduced together with the important concepts of independence, correlation and covariance, marginal and conditional distributions. Techniques for determining distributions of transformations of random variables are discussed. The concept of the sampling distribution and standard error of an estimator of a parameter is presented, together with key properties of estimators. Large sample results concerning the properties of estimators are presented with emphasis on the central role of the Normal distribution in these results. General approaches to obtaining estimators of parameters are introduced. Numerical simulation and graphing with Stata is used throughout to demonstrate concepts.

Unit details and rules

Managing faculty or University school:

Public Health

Code BSTA5023
Academic unit Public Health
Credit points 6
Assumed knowledge:

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

  • LO1. Analyse probability and conditional probability of an event by applying a probabilistic model for an experiment
  • LO2. Apply a range of strategies to find and interpret the moments of discrete and continuous random variables including their expected values and variances
  • LO3. Apply the Law of Large Numbers (LLN) and the Central Limit Theorem (CLT) to find asymptotic distribution of a sample mean
  • LO4. Analyse a bivariate probability distribution to find and interpret corresponding covariances, correlations, marginal and conditional probability distributions
  • LO5. Apply Markov Chain (MC) theory to practical problems and tasks

Unit availability

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There are no availabilities for this year.
Session MoA ?  Location Outline ? 
Semester 1 2020
Online Camperdown/Darlington, Sydney
Semester 2 Early 2020
Online Camperdown/Darlington, Sydney
Outline unavailable
Semester 1 2021
Online Camperdown/Darlington, Sydney
Semester 2 2021
Online Camperdown/Darlington, Sydney
Semester 1 2022
Online Camperdown/Darlington, Sydney

Modes of attendance (MoA)

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Important enrolment information

Departmental permission requirements

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Additional advice

Refer to the unit of study outline