Unit of study_

Applied Linear Models - STAT3022

Year - 2020

In today's data-rich world more and more people from diverse fields are needing to perform statistical analyses and indeed more and more tools for doing so are becoming available; it is relatively easy to point and click and obtain some statistical analysis of your data. But how do you know if any particular analysis is indeed appropriate? Is there another procedure or workflow which would be more suitable? Is there such thing as a best possible approach in a given situation? All of these questions (and more) are addressed in this unit. You will study the foundational core of modern statistical inference, including classical and cutting-edge theory and methods of mathematical statistics with a particular focus on various notions of optimality. The first part of the unit covers various aspects of distribution theory which are necessary for the second part which deals with optimal procedures in estimation and testing. The framework of statistical decision theory is used to unify many of the concepts. You will apply the theory to various real-world problems using statistical software in laboratory sessions. By completing this unit you will develop the necessary skills to confidently choose the best statistical analysis to use in many situations.

Classes
Three 1 hour lectures, one 1 hour tutorial and one 1 hour computer laboratories per week.

Assessment
2 x assignment (15%), 3 x quizzes (30%), final exam (55%)

Pre-requisites

STAT2X11 and (DATA2X02 or STAT2X12)

Prohibitions

STAT3912 or STAT3012 or STAT3922

Details

Faculty: Science

Semester 1

24 Feb 2020

Department/School: Mathematics and Statistics Academic Operations
Study Mode: Normal (lecture/lab/tutorial) day
Census Date: 31 Mar 2020
Unit of study level: Senior
Credit points: 6.0
EFTSL: 0.125
Available for study abroad and exchange: Yes
Faculty/department permission required? No
Location
Camperdown
More details
Unit of Study coordinator: Dr John Ormerod
HECS Band: 2
Courses that offer this unit

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