Erasmus Summer Programme Courses

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Regression Analysis [ESP09]

Course highlights

EC points

1.4

Start date

16 August 2021

End date

20 August 2021

Course days

Monday to Friday (5 full days)

Course time

From 8:45 till 16:00 CEST

Faculty

Prof. Brian Marx

Course fee

€ 1480

Location

Online

Level

Intermediate

Prerequisites

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

Disciplines

  • Biostatistics
  • Clinical Research
  • Epidemiology
  • Methodology
  • Public Health

Course Materials

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


A laptop or PC with your favorite Statistical Software. The Course will show examples in SPSS, R, and some SAS.

The course notes will also be provided in PDF; based on experience, it is sometimes useful for students to print out the pdf and bind it. In this way, notes can be made directly as the lecture unfolds. The professor follows the course notes quite closely so the textbook is optional.


The following is the optional textbook: 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.
http://www.amazon.com/Applied-Regression-Analysis-Multivariable-Methods/dp/0495384968

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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