Probability Laws Course
Gain mastery over Poisson processes, exponential waiting times, Central Limit Theorem, and alternative count models through hands-on analysis of real Emergency Room arrival data. The course emphasises diagnostic tools, simulation methods, and clear reporting practices to construct reliable and transparent probability models suitable for professional statistical applications in operations research.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
This course equips you with essential tools to model arrivals and waiting times using ER data. You will explore Poisson and other count models, exponential and advanced waiting-time distributions, applications of the Law of Large Numbers and Central Limit Theorem, simulation techniques, bootstrapping methods, and diagnostic assessments. Develop skills to create transparent, reproducible analyses, evaluate assumptions, compare models effectively, and communicate uncertainty for informed real-world decisions.
Elevify advantages
Develop skills
- Poisson and count modelling: construct, diagnose, and improve ER arrival models efficiently.
- Waiting-time modelling: apply exponential distributions and alternatives to actual ER delay datasets.
- CLT and LLN applications in operations: measure ER averages, risks, and extreme probabilities accurately.
- Simulation and bootstrapping: perform rapid Monte Carlo validations and confidence intervals for small samples.
- Model validation and reporting: verify assumptions and produce clear, robust analytical reports.
Suggested summary
Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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