Credit Points 10

Legacy Code 301114

Coordinator Franco Ubaudi Opens in new window

Description This subject covers concepts of data centric thinking. The main areas discussed are; Populations and Samples; Sampling concepts; Types of Data; Descriptive Methods; Estimation and Inference; and Modelling. The subject takes a computational and nonparametric approach, before discussing theoretical concepts and Normal distribution theory as large sample approximations.

School Computer, Data & Math Sciences

Discipline Statistics

Student Contribution Band HECS Band 1 10cp

Check your fees via the Fees page.

Level Postgraduate Coursework Level 7 subject

Restrictions

Students must be enrolled in a postgraduate program.

Assumed Knowledge

Undergraduate degree with some statistical content (1 subject) is useful.

Learning Outcomes

On successful completion of this subject, students should be able to:

1. Describe types of data and the relevance to real world examples
2. Design data collection strategies that provide unbiased and reliable data
3. Apply appropriate computer based strategies to estimate population parameters of interest
4. Analyze data to make inferences about populations
5. Create and evaluate simple predictive models, using computer software
6. Evaluate literature and identify common statistical mistakes.

Subject Content

1. The Types, Description and Exploration of Data
2. Collecting Good Data
3. Probability Theory
4. Computer assisted Estimation and Inference
5. Linear Modelling
6. Large Samples and Normal Theory
7. Common Statistical Mistakes