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Probability and Distribution Theory (POPH90148)
Graduate courseworkPoints: 12.5Online
Please refer to the return to campus page for more information on these delivery modes and students who can enrol in each mode based on their location.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 1
Melbourne School of Population and Global Health
OR
Currently enrolled students:
- General information: https://ask.unimelb.edu.au
- Email: Contact Stop 1
Future Students:
- Further Information: MSPGH Website
- Email: Enquiry Form
Semester 2
Melbourne School of Population and Global Health
OR
Currently enrolled students:
- General information: https://ask.unimelb.edu.au
- Email: Contact Stop 1
Future Students:
- Further Information: MSPGH Website
- Email: Enquiry Form
Overview
Availability | Semester 1 - Online Semester 2 - Online |
---|---|
Fees | Look up fees |
This subject 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, and 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 are used throughout to demonstrate key concepts.
Intended learning outcomes
On completion of this unit, students should be able to:
- Demonstrate an understanding of the meaning and laws of probability
- Recognise common probability distributions and their properties
- Apply calculus-based tools to derive key features of a probability distribution, such as mean and variance
- Obtain mean, variance and the probability distribution of transformations of random variables
- Manipulate multivariate probability distributions to obtain marginal and conditional distributions
- Understand properties of parameter estimators and the usefulness of large sample approximations in statistics
- Appreciate the role of simulation in demonstrating and explaining statistical concepts.
Generic skills
Independent problem solving, facility with abstract reasoning, clarity of written expression, sound communication of technical concepts.
Last updated: 3 November 2022