161251

# Regression Modelling

Common data analysis and regression techniques for application in science, business and social science. Topics include simple and multiple regression; linear models with categorical explanatory variables; model diagnostics; inference for linear models; polynomial regression; models for time dependence; methods for variable selection; non-linear and weighted regression.

## Course code

Qualifications are made up of courses. Some universities call these papers. Each course is numbered using six digits.

161251

## Level

The fourth number of the course code shows the level of the course. For example, in course 219206, the fourth number is a 2, so it is a 200-level course (usually studied in the second year of full-time study).

200-level

## Credits

Each course is worth a number of credits. You combine courses (credits) to meet the total number of credits needed for your qualification.

15

Statistics

## Course planning information

### Prerequisite courses

Complete first
1611xx or 297101

You need to complete the above course or courses before moving onto this one.

### Restrictions

Similar content
161221

You cannot enrol in this course if you have passed (or are enrolled in) any of the course(s) above as these courses have similar content or content at a higher level.

### General progression requirements

You must complete at least 45 credits from 100-level before enrolling in 200-level courses.

## Learning outcomes

What you will learn. Knowledge, skills and attitudes you’ll be able to show as a result of successfully finishing this course.

• 1 Explore and describe characteristics of quantitative and categorical data and interrelationships among variables.
• 2 Develop appropriate regression models for data analysis, make inferences about the model parameters, and interpret these in context.
• 3 Critically assess whether a regression model adequately describes how one or more explanatory variables affect a response variable, and propose alternative approaches.
• 4 Create and explain analysis of variance tables, and use them to test hypotheses about model parameters.
• 5 Compare regression models and select a subset of explanatory variables that explain variation in a response.
• 6 Use suitable statistical software to explore data and apply regression models.

Learning outcomes can change before the start of the semester you are studying the course in.

## Assessments

Assessment Learning outcomes assessed Weighting
Written Assignment 1 2 3 4 5 6 25%
Written Assignment 1 2 3 4 5 6 25%
Test 1 2 3 4 5 6 25%
Exam (centrally scheduled) 1 2 3 4 5 6 25%

Assessment weightings can change up to the start of the semester the course is delivered in.

You may need to take more assessments depending on where, how, and when you choose to take this course.

### Explanation of assessment types

Computer programmes
Computer animation and screening, design, programming, models and other computer work.
Creative compositions
Animations, films, models, textiles, websites, and other compositions.
Exam College or GRS-based (not centrally scheduled)
An exam scheduled by a college or the Graduate Research School (GRS). The exam could be online, oral, field, practical skills, written exams or another format.
Exam (centrally scheduled)
An exam scheduled by Assessment Services (centrally) – you’ll usually be told when and where the exam is through the student portal.
Oral or performance or presentation
Debates, demonstrations, exhibitions, interviews, oral proposals, role play, speech and other performances or presentations.
Participation
You may be assessed on your participation in activities such as online fora, laboratories, debates, tutorials, exercises, seminars, and so on.
Portfolio
Creative, learning, online, narrative, photographic, written, and other portfolios.
Practical or placement
Field trips, field work, placements, seminars, workshops, voluntary work, and other activities.
Simulation
Technology-based or experience-based simulations.
Test
Laboratory, online, multi-choice, short answer, spoken, and other tests – arranged by the school.
Written assignment
Essays, group or individual projects, proposals, reports, reviews, writing exercises, and other written assignments.

## Textbooks needed

There are no set texts for this course.