Logistic Regression

Dates: 12 February 2020
Instructor: Maria Pampaka
Level: Introductory 
Fee: £195 (£140 for those from educational, government and charitable institutions). 

CMI offers up to five subsidised places at a reduced rate of £60 per course day 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. Applications for bursaries must be submitted at least two weeks in advance of the course date; applications submitted after this time will not be accepted. Retrospective applications cannot be made if courses have already taken place or payment has already been made.

Please click here to make a booking. If you are applying for a subsidised place, select the £60 University of Manchester option on the booking form. For queries about methods@manchester bursaries, contact methods@manchester.ac.uk (please note, you must have a confirmed place on the course before requesting a bursary application form). For any other queries about short courses, please contact cmi-shortcourses@manchester.ac.uk.

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


This course examines the fitting of models to predict a binary response variable from a mixture of binary and interval explanatory variables, using STATA software.

The approach is illustrated using examples from a social science perspective, including cases where logistic regression models are used as a means of analysing tabular data where one of the dimensions of the table is a two-category outcome variable.

You will also learn how to fit a logistic regression model, and how to interpret the results.


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

  • Understand the concepts of odds and odds ratios.
  • Generate odds for given contingency tables. 
  • Understand the basic theory behind binary logistic regression. 
  • Run and interpret a logistic regression model. 
  • Interpret Log Likelihoods to evaluate models.
  • Choose between different models.


Participants should have:

  • a basic familiarity with statistical software;
  • an understanding of basic data analysis techniques and concepts such as cross-tabulations, graphing, variance, significance testing and correlation;
  • an understanding of linear regression would be helpful but not essential. 

The course is designed for users of survey data with some experience in data analysis who want to expand their understanding of more sophisticated techniques.

About the instructor