## Handbook home

# Statistical Techniques in Insurance (ACTL90008)

Graduate courseworkPoints: 12.5On Campus (Parkville)

## About this subject

- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable (login required)(opens in new window)

## Contact information

## Overview

Availability | Semester 2 |
---|---|

Fees | Look up fees |

Topics include multiple linear regression; Spearman´s and Kendall´s measures of correlation; principal component analysis; generalised linear models; bootstrap method; Bayesian statistics; credibility theory.

## Intended learning outcomes

On successful completion of this subject a student should be able to:

- Be able to use exploratory data analysis techniques to reduce dimensionality of complex data sets and to determine the correlation for multivariate data.
- Describe and explain multiple linear regression models.
- Explain the fundamental concepts of a generalised linear model (GLM), and describe how a GLM may apply.
- Use statistical software such as R to fit regression and generalised linear models to a data set and interpret the results and to write simple functions to complete routine tasks.
- Be able to apply the bootstrap method to estimate properties of an estimator.
- Explain the fundamental concepts of Bayesian statistics and apply these concepts to derive Bayesian estimators.
- Describe and apply the fundamental concepts of credibility theory.
- Apply pre-requisite mathematical and statistical concepts to the solution of problems on the above topics.

## Generic skills

High level of development:

- written communication;
- problem solving;
- statistical reasoning;
- application of theory to practice;
- synthesis of data and other information;
- evaluation of data and other information;
- use of computer software.

Last updated: 31 January 2024