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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 models. This is an ideal course for anyone who wishes to further his medical education by getting a better understanding of data analysis.

Learning Objectives

have insight into the √n law and its consequences for sample size
have insight into 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 (x 2 test for goodness-of-fit, Pearson’s x 2 test and McNemar’s x 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.

Learning methods


Weblectures

Assessment


An assessment will be part of this course

Course Deadlines

Please note that you are required to hand in assignments during some of the learning units in this course:

Week 0
Sunday before start date - introduce yourself

Week 1
Sunday – complete Learning Unit 1

Week 2
Sunday – complete Learning Unit 2

Week 3
Sunday – complete Learning Unit 3

Week 4
Sunday – complete Learning Unit 4

Week 5
Sunday – complete Learning Unit 5

Week 6
Sunday – complete Learning Unit 6

Week 7
Sunday – complete Learning Unit 7

Week 8
Sunday – complete Learning Unit 8

Week 9
Sunday – complete Learning Unit 9

Week 10
Sunday – complete Learning Unit 10

Week 11
Sunday – complete Learning Unit 11

Week 12
Sunday – complete Learning Unit 12

Week 13
Monday – Final Exam

Examination

This course includes an exam that primarily consists of essay questions, which is the only part of the course that is not online. The exam dates and times will be announced on the Elevate website.

You are allowed to redo the exam once. If you are able and willing to take the exam in Utrecht, the Netherlands, we are available to proctor the exam for you without any costs. If you have to take the exam from a different location, you need a proctor. This proctor may ask you to pay for their expenses.

Please read more about proctoring on our specific webpage. The exam is not compulsory. However, if you want to receive the Course Certificate and the credits, it is obligatory to take the exam.

Entry Requirements

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)

Please note

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.

Course is offered by

Program taught in:
  • English

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This course is Online
Start Date
Duration
12 weeks
Part-time
Price
1,585 EUR
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