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Regression Modelling G (6557.4)

Level: Graduate Level
Credit Points: 3
HECS Bands: 2, 4
Faculty: Faculty of Science and Technology
Discipline: Academic Program Area - Technology


Unit Outlines

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  • Semester 1, 2019, ON-CAMPUS, BRUCE (185238) - View
  • Semester 1, 2018, ON-CAMPUS, BRUCE (181726) - View
  • Semester 2, 2016, ON-CAMPUS, BRUCE (151232) - View

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This unit explores linear regression techniques for examining relationships between a variety of variables, including both continuous and discrete response variables. Emphasis will be placed on the practical aspects of analysing large data sets, fitting a model and assessing a model using a statistical package. The simple regression model will be reviewed. Multiple regression models will be introduced, together with logistic regression and other generalised linear models. Applications to business, natural and social sciences and other areas will be illustrated.

Learning Outcomes

On completion of this unit, students will be able to:

1. Describe the principles of linear modelling in data analysis;

2. Formulate an appropriate model;

3. Estimate the parameters of a model using a statistical computer package;

4. Apply and explain statistical inference to a model using a statistical computer package;

5. Evaluate the appropriateness and validity of a model;

6. Interpret the results of an estimated model and predict the consequences of these results;

7. Produce the results of analyses in a form which is suitable for publication; and

8. Apply important extensions to the linear regression model.

Assessment Items



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