The University of Newcastle, Australia
Available in 2019

Course handbook

Description

How do we model data of very different types in a consistent way? This course explores generalized linear models and illustrates how methods for analysing continuous and categorical data fit into this framework.


Availability2019 Course Timetables

Callaghan

  • Semester 1 - 2019

Learning outcomes

On successful completion of the course students will be able to:

1. Understand and use the principles of statistical modelling;

2. Have a unified conceptual and theoretical framework for many of the most commonly used statistical methods including multiple linear regression, analysis of variance and logistic regression;

3. Develop skills in statistical computing, specifically in the R statistical programming language and R graphics.

4. Write up a report/project on an analysis of a data set and provide a clear report of results with critical interpretation as based on R code, R outputs and theoretical understanding of theory given in the lectures.


Content

Topics include:

  • Linear models
  • Model fitting as an approach to statistical analysis
  • Exponential family of distributions
  • Maximum likelihood estimation
  • Inference methods based on model fitting
  • Generalised Linear Models
  • Models for continuous data (regression analysis of variance)
  • Models for categorical data (logistic regression for nominal and ordinal data, Poisson regression and log-linear models)
  • Generalised Additive models

Assumed knowledge

STAT3030Generalised Linear ModelsHow do we model data of very different types in a consistent way? This course explores generalized linear models and illustrates how methods for analysing continuous and categorical data fit into this framework.

FSCITFaculty of Science724School of Mathematical and Physical Sciences1030005940Semester 1 - 2019CALLAGHANCallaghan2019STAT2010 Fundamentals of Statistics, STAT2000 Applied statistics and Research MethodsTopics include: Linear models Model fitting as an approach to statistical analysis Exponential family of distributions Maximum likelihood estimation Inference methods based on model fitting Generalised Linear Models Models for continuous data (regression analysis of variance) Models for categorical data (logistic regression for nominal and ordinal data, Poisson regression and log-linear models) Generalised Additive models YOn successful completion of this course, students will be able to:1Understand and use the principles of statistical modelling;2Have a unified conceptual and theoretical framework for many of the most commonly used statistical methods including multiple linear regression, analysis of variance and logistic regression;3Develop skills in statistical computing, specifically in the R statistical programming language and R graphics.4Write up a report/project on an analysis of a data set and provide a clear report of results with critical interpretation as based on R code, R outputs and theoretical understanding of theory given in the lectures. Written Assignment: Assignment 1Written Assignment: Assignment 2Presentation: Oral for ProjectProject: Written ProjectFormal Examination: Examination - Formal CallaghanComputer LabFace to Face On Campus2hour(s)per Week for0Full Term0LectureFace to Face On Campus2hour(s)per Week for0Full Term0


Assessment items

Written Assignment: Assignment 1

Written Assignment: Assignment 2

Presentation: Oral for Project

Project: Written Project

Formal Examination: Examination - Formal


Contact hours

Callaghan

Computer Lab

Face to Face On Campus 2 hour(s) per Week for Full Term

Lecture

Face to Face On Campus 2 hour(s) per Week for Full Term