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Course module: BMB403314
BMB403314
Classical Methods in Data Analysis
Course info
Course codeBMB403314
EC6
Course goals
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):  This course is taught several times per year, during fulltime weekdays according to the following Schedule (Face-2-Face in Period A, Online in Period B):
Education form Startdate Enddate Registration period
Face-2-Face 23-10-2023 17-11-2023 BMS_P1_A
Online* 27-11-2023 01-03-2024 BMS_P2_B
Online* 06-05-2024 26-07-2024 BMS_P4_B
*Online courses are only available for Epidemiology students following a full online programme.

Max 70 students in the Face-2-Face edition

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

Registration:
Single courses — MSc Epidemiology (msc-epidemiology.nl)

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
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