Applied Linear Models

Statistical linear models 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; and weighted regression.

Course code

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



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).



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


Course planning information

Course notes

Students who have not yet passed one of 161.122 or 161.220 or 233.214 may enrol in 161.220 in the same semester as 161.221, provided that they meet the prerequisites for 161.220. Access to a Windows PC and an approved statistics package are required for analysis of data.

Students must achieve at least 40% in the final exam to be eligible to pass.

Prerequisite courses

Complete first
(One of (161122 or 161220 or 233214) and one of (160101 or 160102 or 160105)) or one of 161101, 161120 or 161130

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


Choose just one

The courses listed above have similar content to this one meaning you can only enrol in this course or one of the listed courses. Only one of the courses can be credited towards your qualification.

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 Develop appropriate linear models for data analysis.
  • 2 Critically assess whether a linear model adequately describes how one or more explanatory variables affect a response variable.
  • 3 Make inferences about the model parameters, and interpret these in context.
  • 4 Create and explain analysis of variance tables, and use them to test hypotheses about model parameters.
  • 5 Compare nested models and select a subset of explanatory variables that explain variation in a response.
  • 6 Use suitable statistical software to apply linear models.

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


Assessment Learning outcomes assessed Weighting
Written Assignment 1 2 3 6 15%
Written Assignment 1 2 3 4 5 6 15%
Written Assignment 1 2 3 4 5 6 15%
Exam College/GRS-based (not centrally scheduled) 1 2 3 4 5 55%

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.
You may be assessed on your participation in activities such as online fora, laboratories, debates, tutorials, exercises, seminars, and so on.
Creative, learning, online, narrative, photographic, written, and other portfolios.
Practical or placement
Field trips, field work, placements, seminars, workshops, voluntary work, and other activities.
Technology-based or experience-based simulations.
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.

Course delivery details

No offerings available

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