# STAT362 Probability and Inference 2

Theory of ordinary least squares, maximum likelihood estimation and inference, hypothesis testing, Bayesian inference.

This paper continues the theoretical development begun in STAT 261. The tools developed by statisticians for analysing data have become a major factor in the advancement of scientific knowledge. Why are these tools so useful? The reason is that they are based on an agreed system of mathematical and statistical reasoning. In order to be confident that the methods used by a statistician are reliable, we need an understanding of this theory.

Paper title Probability and Inference 2 STAT362 Statistics 0.1500 18 points Second Semester \$868.95 \$3,656.70
Prerequisite
STAT 261 and MATH 170
Schedule C
Arts and Music, Science
Eligibility
All students majoring in Statistics under the Statistics Theme must take this paper. It is strongly recommended for all statistic students. STAT 261 and STAT 362 should also be considered by students with a strong mathematics background interested in a career in scientific research.
Contact
twang@maths.otago.ac.nz
Teaching staff
mparry@maths.otago.ac.nz
Paper Structure
Main topics:
• The general linear model
• The likelihood function
• Bayesian inference
• Maximum likelihood estimation
• Hypothesis testing using the likelihood function
• Model selection using the likelihood function
Teaching Arrangements
Five 50-minute lectures per fortnight. One 2-hour tutorial a week.
Textbooks
Textbooks are not required for this paper.

Useful references:
• Mathematical Statistics with Applications by Wackerly, Mendenhall and Scheaffer
• An Introduction to Mathematical Statistics and its Applications by Larsen and Marx
A full set of lecture notes is available on the Departmental website.
Course outline
View course outline for STAT 362
Communication, Critical thinking, Information literacy.
Learning Outcomes
Students who successfully complete the paper will develop an understanding of key concepts in mathematical statistics.

## Timetable

### Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Other

#### Lecture

Stream Days Times Weeks
Attend
L1 Tuesday 10:00-10:50 28-34, 36-41
Thursday 10:00-10:50 28-34, 36-41
Friday 10:00-10:50 28-34, 36-41

#### Tutorial

Stream Days Times Weeks
Attend
T1 Wednesday 15:00-16:50 28-34, 36-40