Regression Analysis [ESP09]

Course highlights

EC points

1.4

Start date

16-08-2021

End date

20-08-2021

Course days

Monday to Friday (5 full days)

Faculty

Prof. Brian Marx

Course fee

€ 1480

Location

Online

Level

Intermediate

Prerequisites

Course Introduction to Data-analysis (ESP03) or equivalent knowledge.

Disciplines

  • Biostatistics

Application

Go to the ESP website

Detailed information about this course:

Description

Faculty: Prof. Brian Marx, PhD

This intermediate level course aims at providing theoretical and practical training for epidemiologists, clinicians and other professionals of related health disciplines in statistical modeling with particular emphasis on straight line linear and multiple regression. Included topics are: review of straight line regression and correlation, ANOVA for straight line regression, appropriateness of straight line model, polynomial regression, multiple regression analysis, partial F-test, dummy/indicator variables, statistical interaction, comparing straight line regressions, analysis of covariance, estimation and interpretation, goodness-of-fit, model selection, collinearity and outlier diagnostics. Additionally, extensions to some generalized linear models, such as logistic (binomial) regression and Poisson regression, will be introduced and interpreted through examples-- thus helping to bridge the material presented in ESP66 (Logistic Regression).


//Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

Objectives

  • Students will learn the fundamental methods of statistical regression modeling for continuous response variables;
  • Students will learn how to build and interpret a variety of multiple regression models, including models with continuous, nominal/indicator, and polynomial explanatory regressor variables;
  • Students will become familiar with outlier and collinearity diagnostics to refine models, as well as statistical software packages for computing multiple regression models.

Participant profile

This intermediate level course aims at providing theoretical and practical training for (bio)statisticians, epidemiologists, clinicians and other professionals or resesarchers of related health disciplines in statistical modeling.

Assessment

Attendance


Reduction on fees

PLEASE NOTE THIS DOES NOT APPLY TO THE RESEARCH MASTERS (120 EC points)

No fees are charged for participation of Erasmus MC PhD candidates

Please note that in case of cancellation or no show, the cancellation policy applies based on the full course fee.

50% reduction on our fees for participating institutes of NIHES

This 50% reduction is offered to participants formally appointed at the departments or sections participating in NIHES.

University Medical Center Rotterdam/Erasmus University Rotterdam:

  • Department of Epidemiology
  • Department of Public Health
  • Department of Psychiatry, Section of Medical Psychology and Psychotherapy
  • Department of Child and Adolescent Psychiatry/Psychology
  • Department of Medical Informatics
  • Department of General Practice
  • Erasmus School of Health Policy & Management
  • Generation R

50% reduction on our fees for NIHES affiliated partners

This 50% reduction is offered to participants formally appointed at the following NIHES affiliated partners:

University Medical Center Rotterdam/Erasmus University Rotterdam, departments of:

  • Dermatology
  • General Paediatrics
  • Plastic and Reconstructive Surgery
  • Rehabilitation Medicine

UMIT, Department of Public Health, Health Services Research and Health Technology Assessment.

25% reduction on our fees for Erasmus MC employees

For participants formally appointed at all departments of Erasmus MC (except the departments mentioned above and Erasmus MC PhD candidates).

25% reduction for all (international) PhD students without formal appointment at Erasmus MC

Upon receipt of your application you will receive a request to upload proof of enrollment as a PhD student.