Erasmus Summer Programme Courses
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Regression Analysis [ESP09]
14 August 2017
18 August 2017
Monday to Friday (5 full days)
From 8:45 till 16:00
Prof. Brian Marx
Erasmus MC, Rotterdam NL
Course Introduction to Data-analysis (ESP03) or equivalent knowledge
Online, download instructions will be sent in August by e-mail.
The following texts (book) will be used:
Applied Regression Analysis and Other Multivariable Methods by Kleinbaum, Kupper, Muller and Nizam.
This book is available via Amazon. Please note: 4th Ed is perfectly fine for this course.
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NIHES opened important doors for me in the world of epidemiologyRead the full story
Detailed information about this course:
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.
Written exam on Friday 2 September 2016 (only for NIHES MSc students and for ‘keuzevak students’), date resit is to be announced. Course materials are allowed during the examination. If other students wish to do this exam, they have to pay a fee of €75,- per exam. Credits are 1.9 ECTS when you take the exam, instead of 1.4 ECTS.
This course is equivalent to Regression Analysis for Clinicians (EWP23).
- 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.
Attendance, Written exam