Inference and Models
Inference and Models
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.
Target Group
Our courses are aimed at clinical researchers, nurses, general practitioners, and other health professionals who want to improve their skills in epidemiology, statistics and (clinical) research.
Learning Objectives
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
Face to face
1 week, fulltime