IB Maths AI HL 4.17 – The Poisson Distribution Notes

IB Maths AI HL 4.17 Notes – The Poisson Distribution

This IB Maths AI HL 4.17 resource covers The Poisson Distribution and is fully aligned with the IB Applications and Interpretation HL syllabus

Students learn when the Poisson model is appropriate, including events occurring independently at a constant average rate over a fixed interval. The probability formula ( P(X = k) = \frac{e^{-\lambda}\lambda^k}{k!} ) is introduced, along with the key results that the mean and variance are both equal to ( \lambda ).

The resource also develops the important result that the sum of two independent Poisson variables is also Poisson, with parameter ( \lambda_1 + \lambda_2 ).

Structured practice questions help students calculate probabilities, interpret the parameter ( \lambda ), and apply the model in context.

Ideal for IB Maths AI HL teachers teaching advanced discrete probability models.