Principles of Statistical Inference
|Course code BIOS6050||Units 10||Level 6000||Faculty of Health and MedicineSchool of Medicine and Public Health|
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)
Available in 2014
|Objectives||At the completion of this course the student will:|
1. Have a deeper understanding of fundamental concepts in statistical inference and their practical interpretation and importance in biostatistical contexts
2. Understand the theoretical basis for frequentist and Bayesian approaches to statistical inference
3. Be able to develop and apply parametric methods of inference, with particular reference to problems of relevance in biostatistical contexts
4. Have the theoretical basis to understand the justification for more complex statistical procedures introduced in subsequent units
5. Have an understanding of basic alternatives to standard likelihood-based methods, and be able to identify situations in which these methods are useful.
|Content||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.|
|Assumed Knowledge||Mathematical Background for Biostatistics (BIOS6040);|
Probability and Distribution Theory (BIOS6170).
|Modes of Delivery||Distance Learning : Paper Based|
|Teaching Methods||Self Directed Learning|
|Contact Hours||Self Directed Learning: for 6 hour(s) per Week for Full Term|
|Timetables||2014 Course Timetables for BIOS6050|