# Linear Models

## Course Description

• ### Course Name

Linear Models

• ### Area of Study

Mathematics, Statistics

• ### Language Level

Taught In English

• ### Prerequisites

Pre-requisites: ST1SIM Statistical Inference and Modelling
Non-modular pre-requisites:

• ### Course Level Recommendations

Upper

ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

### Hours & Credits

• ECTS Credits

5
• Recommended U.S. Semester Credits
3
• Recommended U.S. Quarter Units
4
• ### Overview

Summary module description:
This module covers the most common models used in statistics: multiple linear regression for observational studies and completely randomised (blocked) designs for planned studies.

Aims:
Linear models are used widely in statistics. The most common models will be reviewed and their relationship to the general linear model explored. The main aim of the module is to present a standard approach for fitting linear models to data and for comparing alternative linear models with one another. The module also aims to provide the skills to develop and test linear models appropriate for a range of practical problems.

Assessable learning outcomes:
On completion of this module students will have acquired:
knowledge of the basic theory associated with the general linear model and the principles of analysis of variance;
the ability to fit regression models to data, interpret them and check their adequacy;
an awareness of the role of regression modelling in the analysis of data from designed experiments;
the ability to use SAS to fit linear models and check their adequacy.

Outline content:

Simple linear regression and the completely randomised design.
Randomised block designs.
The General Linear Model for multiple regression: definition and matrix notation.
Model checking: residual analysis, influential observations, transformations.
Further topics: polynomial regression, indicator variables, variable selection, multicollinearity.
Use of SAS to fit linear models.

Brief description of teaching and learning methods:
Lectures, supported by problem sheets, PC practicals and tutorials.

Summative Assessment Methods:
Written exam 80%
Set exercise 20%

Other information on summative assessment:
Two assignments and one examination.

Formative assessment methods:
Problem sheets.

Penalties for late submission:
The Module Convener will apply the following penalties for work submitted late, in accordance with the University policy.
where the piece of work is submitted up to one calendar week after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for the piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: http://www.reading.ac.uk/web/FILES/qualitysupport/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Length of examination:
2 hours

Requirements for a pass:
A mark of 40% overall

### Course Disclaimer

Courses and course hours of instruction are subject to change.

Some courses may require additional fees.

Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.

ECTS (European Credit Transfer and Accumulation System) credits are converted to semester credits/quarter units differently among U.S. universities. Students should confirm the conversion scale used at their home university when determining credit transfer.

Please reference fall and spring course lists as not all courses are taught during both semesters.

Please note that some courses with locals have recommended prerequisite courses. It is the student's responsibility to consult any recommended prerequisites prior to enrolling in their course.

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