# STATS 330 : Statistical Modelling

## Science

### Course Prescription

Application of the generalised linear model and extensions to fit data arising from a range of sources including multiple regression models, logistic regression models, and log-linear models. The graphical exploration of data.

### Course Overview

STATS 330 further develops ideas introduced in STATS 201/208, giving a synthesis and broader understanding of generalised linear models and related methods. Simulation-based procedures, including bootstrapping and cross-validation, are introduced as a means to provide robust inference, to investigate consequences of assumption violations, and to solve goodness-of-fit and model-selection problems. Particular focus is placed on how the modelling procedure varies depending on whether the analysis aims to explain an underlying process or predict future observations. Students will learn to implement all methods taught in R, the widely used, open-source software environment for statistical computing. Emphasis is on practical application, providing students with a versatile statistical toolbox useful for a range of fields in both academia and industry, including almost all subjects in business and economics, along with any experimental or social science. It is also a useful complement to computer science.

### Course Requirements

Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209

### Capabilities Developed in this Course

 Capability 1: Disciplinary Knowledge and Practice Capability 2: Critical Thinking Capability 3: Solution Seeking Capability 4: Communication and Engagement Capability 5: Independence and Integrity
Graduate Profile: Bachelor of Science

### Learning Outcomes

By the end of this course, students will be able to:
1. Define the generalised linear model and describe its underlying assumptions (Capability 1)
2. Explain and use a variety of statistical tools and procedures that determine the appropriateness of a fitted statistical model (Capability 1)
3. Summarise an appropriate modelling procedure, outlining how this is driven by the aims of the analysis (Capability 1)
4. Identify an appropriate candidate model to fit to a particular data set that is capable of answering the questions of interest (Capability 3)
5. Evaluate the appropriateness of a fitted statistical model, and take sensible steps to improve a model that is found to be inappropriate (Capability 2 and 3)
6. Write their own R code to carry out each step of the modelling procedure (Capability 1 and 5)
7. Communicate the findings of an analysis accurately and concisely (Capability 4)

### Assessments

Assessment Type Percentage Classification
Assignments 20% Individual Coursework
Final Exam 50% Individual Examination
Quizzes 10% Individual Coursework
Test 20% Individual Coursework
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7
Assignments
Final Exam
Quizzes
Test

### Key Topics

Generalised Linear Models
Model Selection
Simulation
Confidence and Prediction intervals
Bootstrapping

### Special Requirements

The test may be held in the evening.

This course is a standard  15 point course and students are expected to spend 150 hours per semester involved in each 15 point course that they are enrolled in.

For this course, you can expect 3 hours of lectures/week, an optional weekly  2  hour tutorial, and approximately 5  hours of reading and thinking about the content for  assignments and/or test/exam preparation.

### Delivery Mode

#### Campus Experience

Lectures will be available as recordings. Other learning activities including labs will not be available as recordings.
Online (Zoom) office hours can be arranged as required.
Attendance on campus is not required for the test.
Attendance on campus is required for the exam.
The activities for the course are scheduled as a standard weekly timetable.

This course is also available for remote students.

### Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Course notes are supplied to students via CANVAS.

### Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

### Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

### Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

### Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page .

This should be done as soon as possible and no later than seven days after the affected test or exam date.

### Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

Level 1: Delivered normally as specified in delivery mode
Level 2: You will not be required to attend in person. All teaching and assessment will have a remote option.
Level 3 / 4: All teaching activities and assessments are delivered remotely

### Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter .

### Disclaimer

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 09/11/2021 03:05 p.m.