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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:
Medicine and Health
| Study level | Postgraduate |
|---|---|
| Academic unit | Public Health |
| Credit points | 6 |
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Prerequisites:
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BSTA5001 |
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Corequisites:
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None |
| Prohibitions:
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BSTA5100 |
| Assumed knowledge:
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None |
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
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Important enrolment information
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