HM2004 Practical Statistics
KTH Royal Institute of Technology
Having basic knowledge in Mathematical Statistics and be eligible to the programme TPLVM
The largest part is learning how to use Stochastic Simulation for risk analysis and predictions. The risk methods like Fault Tree, Failure Mode Effect Analysis are taught as well as Two-level multi-factor Design of Experiments using statistical methods and an orthogonal matrix.
A vital part of the content is the translation of collected data into the possible statistical distribution to be used in the stochastic simulation.
The aim is to give the participants the ability to use statistics and related areas in an advanced form for predictions and risk analysis as input for decisions.
The participant is expected after the study course to be able to:
- Select proper distributions for the stochastic simulation from available data
- Make stochastic simulation models using Monte Carlo technique
- Explain what the simulation result is revealing about risk and opportunity
- Use the most common qualitative risk analysis methods as a complement to the numerical
- Make a complete risk analysis using both qualitative and quantitative data
- Use statistical design of experiments to evaluate multi-factorial dependencies in the risk analysis and the predictions
Write risk analysis reports which also laymen can
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