Classical Methods in Data Analysis
This online medical course, offered by the MSc Epidemiology program of the UMC Utrecht and Utrecht University, provides an understanding of 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. This is an ideal course for anyone who wishes to further his medical education by getting a better understanding of data analysis.
For more information, check the MSc Epidemiology website.
- have insight in the √n law and its consequences for sample size
- have insight in the general principles of decision procedures (“testing”), and be able to apply these procedures in practice using common statistical packages (SPSS, R)
- understand 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)
- know in which situations these techniques can be applied and the conditions that should be met to obtain reliable results using these techniques
- be able to apply these techniques using common statistical packages (SPSS, R)
- have insight in the Kolmogorov Smirnov test (normal distribution) and the Fisher test for equality of variances and be able to apply these tests in practice using common statistical packages (SPSS, R)
- understand the results obtained with these techniques, and be able to apply these results in practice (e.g. in answering a study questions
- be familiar with the terms ‘explained variance’ and multi-collinearity
- understand the principles of model reduction in regression analysis
- understand the basic principles of the technique of logistic regression analysis
- be able to choose the appropriate non-parametric technique to be applied in case of non-normally distributed data, and understand the principles of these methods.
Please note that you are required to hand in assignments during some of the learning units in this course:
Sunday before start date - introduce yourself
Sunday – complete Learning Unit 1
Sunday – complete Learning Unit 2
Sunday – complete Learning Unit 3
Sunday – complete Learning Unit 4
Sunday – complete Learning Unit 5
Sunday – complete Learning Unit 6
Sunday – complete Learning Unit 7
Sunday – complete Learning Unit 8
Sunday – complete Learning Unit 9
Sunday – complete Learning Unit 10
Sunday – complete Learning Unit 11
Sunday – complete Learning Unit 12
Monday – Final Exam
Exam edition November 2018
The exam for the November 2018 edition of the course will take place on Monday February 18th, 2019 at 13:00 CET. The re-exam will take place on March 25th.
Exam edition May 2019
The exam for the May 2019 edition of the course will take place on July 29th, 2019.The exact time will be announced as soon as possible. The re-exam will take place on August 26th, 2019.
The exams will be on paper and you will need to arrange a proctor for the exam. If you are able to take the exam in Utrecht, the Netherlands, we will be able to arrange a proctor for you without any costs.
To enroll in this course you need:
- A BSc degree
- To have participated in an introductory statistics course
- A sufficient proficiency in English reading and writing (B1 level of the Common European Framework of Reference)
As this is an online course, you do need access to an internet connection in order to be able to complete assignments and communicate with fellow participants.
- 6 May 2019 – 28 Jul 2019
- 12 weeks
- 14 hrs/wk
- 6.0 EC
- Lectures, computer practicals, self study
1585(Discounts and scholarships may apply)