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Logistic Regression [ESP66]

Course highlights

EC points


Start date

5 August 2024

End date

9 August 2024

Course days

Monday to Friday (5 full days)

Course time

From 8:45 till 16:00 CEST


Prof. Stanley Lemeshow

Course fee

€ 1778


Erasmus MC, Rotterdam NL




A first course in applied statistical methods including linear and multiple linear regression analysis.


  • Biostatistics
  • Advanced Statistics
  • Clinical Epidemiology
  • Clinical Research
  • Methodology
  • Epidemiology

Course Materials

Digitally, download instructions will be sent before the start of the course, by e-mail.

We will be analyzing all data with Stata but code will be made available for students wishing to use R.

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Detailed information about this course:


Faculty: Prof. Stanley Lemeshow, PhD

The aim of this course is to provide theoretical and practical training for biostatisticians, epidemiologists, medical researchers and professionals of related disciplines in statistical modeling with particular emphasis on logistic regression. The increasingly popular logistic regression model has become the standard method for regression analysis of binary, multinomial and ordinal response data in the health sciences. Students will become familiar with statistical software packages and the analysis of a real data sets.


Upon successful completion of the course, students will have the knowledge, comprehension and/or skills to be able to:

  • provide a focused introduction to the logistic regression model and its use in modeling the relationship between a categorical outcome variable and a set of covariates.
  • provide guidelines for effective model building and interpreting the resulting of a fitted model within the context of the applied problem including determination of scale of continuous covariates.
  • provide guidelines for assessing model performance.
  • present a comprehensive discussion of the use of logistic regression modeling for multinomial and ordinal response data and matched case-control data.

Participant profile

Public health and medical researchers interested in understanding and implementing the analysis of data with nominal scale responses.