|Faculty or Section :||Faculty of Health, Engineering and Sciences|
|School or Department :||School of Mathematics, Physics & Computing|
|Student contribution band :||Band 1|
|Grading basis :||Graded|
|Version produced :||23 May 2022|
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 shall 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.
|Semester 2, 2022||Online|