At the end of the course, the student will:
understand the sampling principles of statistical inference
be familiar with the principles of likelihood theory
know the different types of hypothesis tests
know the standard methods of point estimation
know the standard methods of interval estimation
be familiar with numerical methods for statistical inference
know different modeling strategies and when to use them
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| Education form |
Startdate |
Enddate |
Registration period |
| Face-2-Face |
28-3-2022 |
1-4-2022 |
BMS_P3_A |
Contact details: Educational Office Epidemiology
E-mail: msc-epidemiology@umcutrecht.nl
Registration:
Register via https://www.msc-epidemiology.nl/single-courses.html
Course coordinator:
Rene Eijkemans
Course description:
Statistical inference is intended to aid in answering scientific questions about a population, based on a sample from this population, i.e. on data that are subject to variability. The data generating mechanism is described as a probability model that is completely specified except for a limited number of unknown parameters. The questions that can be answered are a) are the data consistent with the model? and b) assuming that a) is fulfilled, what can be concluded about values of the unknown parameters? In this course, the basic principles of statistical inference are presented, with an emphasis on likelihood methods. Methods are illustrated by the classical linear model.
Literature/study material used:
-
Mandatory for students in own Master’s programme:
MIght be for a specialization programme of Epidemiology & Epidemiology Postgraduate
Optional for students in other GSLS Master’s programme:
Yes
Prerequisite knowledge:
Introduction to Statistics
Classical Methods in Data Analysis
Modern Methods in Data Analysis
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