Unit of study_

# BSTA5007: Linear Models

### 2021 unit information

The aim of this unit is to enable students to apply methods based on linear models to biostatistical data analysis, with proper attention to underlying assumptions and a major emphasis on the practical interpretation and communication of results. This unit will cover: the method of least squares; regression models and related statistical inference; flexible nonparametric regression; analysis of covariance to adjust for confounding; multiple regression with matrix algebra; model construction and interpretation (use of dummy variables, parametrisation, interaction and transformations); model checking and diagnostics; regression to the mean; handling of baseline values; the analysis of variance; variance components and random effects. NOTE: Linear Models is an important foundation unit. Students who do not develop a strong grasp of this material will struggle to become successful biostatisticians.

## Unit details and rules

#### Public Health

Code BSTA5007 Public Health 6
 Prerequisites: ? BSTA5023 and (BSTA5011 or PUBH5010 or CEPI5100) BSTA5002 None None

At the completion of this unit, you should be able to:

• LO1. Have a sound understanding of the normal linear model including a theoretical grounding in the principles of least squares and likelihood-based estimation and related statistical inference, to the level of being able to manipulate equations required for deriving formulas for estimates and their standard errors for the standard models.
• LO2. Understand the principles and practice of model checking and diagnostics, and the use of transformations, in particular the log transformation, to improve model fit; understand the appropriate use of analysis of covariance to adjust for confounding; have a good working knowledge of the theory and practice of multiple regression analysis; be familiar with the method of analysis of variance (up to 2 factor models) and its relationship to multiple regression; gain an introductory understanding of nonparametric smoothing for flexible regression modelling, and of the use of variance components and random effects models.
• LO3. Have a strong grasp of practical issues involved in fitting linear models, including the ability to construct defensible models (use of dummy variables, choice of parameterisation, interaction and transformation of variables); demonstrate ability to fit models using modern statistical software and to interpret fitted models in terms that are useful to non-statisticians.

## Unit availability

This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.

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There are no availabilities for this year.
Session MoA   Location Outline
Semester 1 2020
Online Camperdown/Darlington, Sydney
Semester 2 Early 2020
Online Camperdown/Darlington, Sydney
Semester 1 2021
Online Camperdown/Darlington, Sydney
Semester 2 2021
Online Camperdown/Darlington, Sydney

### Modes of attendance (MoA)

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