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Course module: BMB403303
BMB403303
Classical Methods in Data Analysis
Course info
Course codeBMB403303
ECTS Credits6
Category / LevelM (Master)
Course typeCourse
Language of instructionEnglish
Offered byFaculty of Medicine; Graduate School of Life Sciences; Epidemiology;
Contact personC.L.J.J. Kruitwagen
E-mailC.L.J.J.Kruitwagen@umcutrecht.nl
Lecturers
Contactperson for the course
C.L.J.J. Kruitwagen
Other courses by this lecturer
Teaching period
MASTER  (21/08/2017 to 18/08/2018)
Teaching period in which the course begins
MASTER
Time slot-: Not in use
Study mode
Full-time
Course application processadministratie onderwijsinstituut
Enrolling through OSIRISNo
Enrolment open to students taking subsidiary coursesYes
Pre-enrolmentNo
Waiting listNo
Course placement processadministratie onderwijsinstituut
Aims
At the end of this course, the student:
  1. has insight in the √n law and its consequences for sample size;
  2. has insight in the general principles of decision procedures (“testing”), and is able to apply these procedures in practice using common statistical packages (SPSS, R);
  3. understands the principles of the following statistical analysis techniques:
  • student T tests (1-sample, 2-sample and paired);
  • analysis of Variance (1-way and 2-way ANOVA);
  • simple and multiple linear regression analysis;
  • 1-sample, 2-sample and paired proportion tests (χ 2 test for goodness-of-fit, Pearson’s χ 2 test and McNemar’s χ 2 test);
  1. knows in which situations these techniques can be applied and the conditions that should be met to obtain reliable results using these techniques;
  2. is able to apply these techniques using common statistical packages (SPSS, R);
  3. has insight in the Kolmogorov Smirnov test (normal distribution) and the Fisher test for equality of variances and is able to apply these tests in practice using common statistical packages (SPSS, R);
  4. understands the results obtained with these techniques, and is able to apply these results in practice (e.g. in answering a study question);
  5. is familiar with the terms ‘explained variance’ and multi-collinearity;
  6. understands the principles of model reduction in regression analysis;
  7. understands the basic principles of the technique of logistic regression analysis;
  8. is able to choose the appropriate non-parametric technique to be applied in case of non-normally distributed data, and understands the principles of these methods;
  9. is able to apply these techniques using common statistical packages (SPSS, R);
  10. understands the results obtained with these techniques, and is able to apply these results in practice (e.g. in answering a study question).
Content
Period (from – till): Check https://epidemiology-education.nl/

Contact details: Educational Office Epidemiology
E-mail: msc-epidemiology@umcutrecht.nl

Registration:
https://epidemiology-education.nl/ – learning environment

Course coordinator
C.L.J.J. Kruitwagen, MSc
UMC Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht, the Netherlands
 
Faculty
C.L.J.J. Kruitwagen, MSc
UMC Utrecht, Julius Center for Health Sciences and Primary Care.
R.K. Stellato, PhD
UMC Utrecht, Julius Center for Health Sciences and Primary Care.
P. Westers, PhD
UMC Utrecht, Julius Center for Health Sciences and Primary Care.
 
Course description:
This course starts with the basic applications of biostatistics in the analysis of medical research data. Topics are: types of data, location and variability measures, samples and populations, distributions, confidence intervals, hypothesis testing, comparing two or more means or proportions (parametric and non-parametric methods), and relationships between two variables (correlation, simple linear regression). The course also includes an extensive discussion of the multiple linear regression model.

Literature/study material used:
-
 
Mandatory for students in own Master’s programme:
Epidemiology & Epidemiology Postgraduate
 
Optional for students in other GSLS Master’s programme: Yes
 
Prerequisite knowledge:
Introduction to Statistics
Competencies
-
Entry requirements
-
Prerequisite knowledge
The course Introduction to Statistics
Required materials
-
Instructional formats
Computer practical

Lecture

Tests
Final result
Test weight100
Minimum grade-

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