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Cathie Marsh Institute for Social Research (CMI)

Multiple Linear Regression

Dates: 17 January 2019
Instructor: Maria Pampaka
Level: Introductory 
Fee: £195 (£140 for those from educational, government and charitable institutions). 

We offer up to 5 subsidised places at a reduced rate of £60 per course to research staff and students within Humanities at the University of Manchester. These places are awarded in order of application. 

Humanities PGR students at The University of Manchester can apply for a methods@manchester bursary to help cover their costs. All applications will be considered on a case-by-case basis and applicants will be required to provide a supporting statement from their supervisor.

Please contact Joshua Edgar (email: methods@manchester.ac.uk) for an application form and further information.

Please note: this is not guaranteed and is considered on a case by case basis. Please contact us for more information.

Outline

This course provides a thorough grounding in the theory and methods of multiple linear regression including model selection, nonlinear relationships and transformations, dummy variables, interaction terms and assumption testing. The course comprises taught and practical components in about equal proportions.

The course is designed for users of survey data with some experience of data analysis and who are comfortable using statistical software and who want to expand their understanding of more sophisticated techniques.

Objectives

At the end of the course participants should be able to:

  • Run multiple linear regression models on suitable datasets
  • Choose between different models. 
  • Understand the meaning of b and beta coefficients. 
  • Understand and Interpret R2 values. 
  • Create dummy variables, interaction and quadratic terms. 
  • Run and interpret assumptions tests and diagnostics.
  • Understand and interpret multicollinearity

Prerequisites

Participants should have a basic familiarity with statistical software. They should also have an understanding of basic data analysis techniques and concepts such as cross-tabulations, graphing, variance, significance testing and correlation. An understanding of simple linear regression would be helpful but not essential as this will be covered at the beginning of the course.

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