STAT2300 further extends the theory, methods and mechanics that underpin standard statistical analysis from STAT1300. The course builds upon advances in computational power by introducing exciting numerically-driven techniques such as jackknife and bootstrap resampling procedures for investigating and modelling data, thereby equipping students with tools for analysis of data when distributional assumptions are challenging. The exploration of these techniques will principally focus around aspects of estimation and hypothesis testing, including properties and relationships between estimators and tests. The methods developed in the course are demonstrated through examples using R, a leading, open-source statistical programming platform.
The written assignments give you the opportunity to apply the concepts we learn in lectures to a number of problems, some theoretical and others computational involving the use of R. The topics covered will align with the course content covered by that stage of the semester.
Availability2021 Course Timetables
- Semester 2 - 2021
This course replaces the following course(s): STAT3010. Students who have successfully completed STAT3010 are not eligible to enrol in STAT2300.
On successful completion of the course students will be able to:
1. Describe the basic concepts underlying estimation theory and hypothesis testing.
2. Apply traditional and numerical approaches of estimation and hypothesis testing to given data.
3. Explain the underlying assumptions and the applicability of each of the approaches studied.
4. Determine appropriate methods for practical inference problems and apply such methods to data.
- Estimation methods
- Hypothesis Testing
- Evaluating tests
- Monte Carlo Simulation methods for inference
- Randomisation tests
- Bootstrap methods
Students cannot enrol in this course if they have successfully completed STAT3010.
Written Assignment: 3 Written Assignments
Formal Examination: Formal Examination
Face to Face On Campus 2 hour(s) per Week for Full Term
Online 2 hour(s) per Week for Full Term
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