1. Handbook
  2. Subjects
  3. Statistical Techniques in Insurance

Statistical Techniques in Insurance (ACTL90008)

Graduate courseworkPoints: 12.5On Campus (Parkville)

You’re viewing the 2024 Handbook:
Or view archived Handbooks

Overview

Availability
Semester 2
FeesLook 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: 10 September 2024