Probability Laws Course
Master Poisson processes, exponential waits, CLT, and alternative count models using real ER arrival data. Learn diagnostics, simulation, and clear reporting to build reliable, transparent probability models for professional statistical work in healthcare operations.

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 checks. Develop skills to create transparent, reproducible analyses, evaluate model assumptions, compare competing models, and communicate uncertainty effectively to support data-driven decisions in real-world scenarios.
Elevify advantages
Develop skills
- Poisson & count modeling: build, diagnose, and refine ER arrival models fast.
- Waiting-time modeling: fit exponential and alternatives to real ER delay data.
- CLT & LLN for operations: quantify ER averages, risks, and tail probabilities.
- Simulation & bootstrap: run quick Monte Carlo checks and small-sample intervals.
- Model validation & reporting: test assumptions and write clear, defensible results.
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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