| Semester 2, 2023 Online | |
| Units : | 1 |
| School or Department : | School of Mathematics, Physics & Computing |
| Grading basis : | Graded |
| Course fee schedule : | /current-students/administration/fees/fee-schedules |
Staffing
Course Coordinator:
Requisites
Pre-requisite: STA2301
Overview
Methods of Statistical Inference, where conclusions are drawn from data that are subject to random variation, form the basis of substantial decision making within and beyond the field of statistics. This course builds on the fundamentals of statistical principles and probability distributions which were first introduced in the Distribution Theory course. It covers the basic logic and underlying philosophy of statistical inference and extends to estimation of parameters and hypothesis testing procedures. An understanding of the concepts and techniques of this course is highly desirable for a practitioner of statistics.
This course provides the students with a firm grounding in the theory and methods of statistical inference and builds on the material covered in STA2301 Distribution Theory. Students will use a number of statistical procedures useful for both parametric and nonparametric inferences and learn different applications for both. Within this course students will derive statistical procedures from first principles. Furthermore, both point and interval estimation as well as test of hypotheses under the classical framework are covered. The theoretical developments which are established in this course are supported by practical applications.
Course learning outcomes
Upon successful completion of this course students should be able to:
- Identify the principles, processes and methods of statistical inference.
- Evaluate inferential problems for various statistical models.
- Apply appropriate statistical models and methods for real life data analysis.
- Communicate statistical inferences using appropriate terminology for expert and non-expert audiences.
Topics
| Description | Weighting(%) | |
|---|---|---|
| 1. | Sampling Distributions (chi-squared, t- and F- distributions); Central Limit Theorem | 10.00 |
| 2. | Estimation: properties of estimators, methods of maximum likelihood and moments, interval estimation, sample size determination | 20.00 |
| 3. | Hypothesis Testing: concepts, Type I and II errors, normal-based tests of proportions, means and variances, large and small samples, one and two samples, Neyman-Pearson Lemma, likelihood ratio tests, power of a test | 20.00 |
| 4. | One-way analysis of variance: Concept, F-test, Kruskal-Wallis test | 10.00 |
| 5. | Regression: the linear model, matrix approach to ordinary least squares, inference in the linear model | 20.00 |
| 6. | Distribution-Free tests: concepts, one and two sample tests of location, goodness-of-fit tests | 20.00 |
Text and materials required to be purchased or accessed
Student workload expectations
To do well in this subject, students are expected to commit approximately 10 hours per week including class contact hours, independent study, and all assessment tasks. If you are undertaking additional activities, which may include placements and residential schools, the weekly workload hours may vary.
Assessment details
| Description | Group Assessment |
Weighting (%) | Course learning outcomes |
|---|---|---|---|
| Problem Solving 1 | No | 20 | 1,2,3,4 |
| Problem Solving 2 | No | 20 | 1,2,3,4 |
| Problem Solving 3 | No | 20 | 1,2,3,4 |
| Report A1 of 2 | No | 20 | 1,2,3,4 |
| Viva voce A2 of 2 | No | 20 | 1,2,3,4 |