Principles of Statistical Inference
Introduces the core concepts of statistical inference, including estimators, confidence intervals, Type I & II errors and p-values. The emphasis is on the practical interpretation of these concepts in biostatistical contexts, including an emphasis on the difference between statistical and practical significance.
This course is offered in conjunction with the Biostatistics Collaboration of Australia (BCA)
|Faculty||Faculty of Health and Medicine|
|School||School of Medicine and Public Health|
Semester 1 - 2016
(Distance Education - Callaghan)
Semester 2 - 2016 (Distance Education - Callaghan)
Semester 1 - 2017 (Distance Education - Callaghan)
Semester 2 - 2017 (Distance Education - Callaghan)
On successful completion of this course, students will be able to:
The course begins by introducing core concepts of statistical inference, including estimators, confidence intervals, Type I & II errors, and p-values. Concepts in classical estimation theory, including bias and efficiency are discussed. The course will then move on to a general study of the likelihood function, which will be used as a basis for the study of likelihood based methodology, including maximum likelihood estimation and inference based on likelihood ratio, Wald and score test procedures. The Bayesian approach to statistical inference will be studied and contrasted with the classical frequentist approach. Additional areas of study will include nonparametric procedures, exact inference and resampling based methodology.
Distance Education - Callaghan
per Week for
|Timetable||2016 Course Timetables for BIOS6050|