S2.1 - The Binomial and Poisson distributions

The binomial and Poisson Students will be expected to use these distributions to distributions. model a real-world situation and to comment critically on their appropriateness.; Cumulative probabilities by calculation or by reference to tables.; Students will be expected to use the additive property of the Poisson distribution – e.g. if the number of events per minute ∼ Po(λ) then the number of events per 5 minutes ∼ Po(5λ).

Use binomial and poisson distributions to connect the rule to the data and decision in the question.

This matters because binomial and poisson distributions determines what can be inferred or chosen; begin with the stated conditions and keep the conclusion tied to the evidence.

Example: apply binomial and poisson distributions to one small, clearly defined case, show the key step or comparison, and explain the result in words.

Boundary: Binomial and Poisson distributions is not a universal recommendation. Check the syllabus scope, assumptions, units and the limits of the evidence before generalising.

The mean and variance of the No derivations will be required. binomial and Poisson distributions.

Use mean and variance of binomial and poisson distributions to connect the rule to the data and decision in the question.

This matters because mean and variance of binomial and poisson distributions determines what can be inferred or chosen; begin with the stated conditions and keep the conclusion tied to the evidence.

Example: apply mean and variance of binomial and poisson distributions to one small, clearly defined case, show the key step or comparison, and explain the result in words.

Boundary: Mean and variance of binomial and Poisson distributions is not a universal recommendation. Check the syllabus scope, assumptions, units and the limits of the evidence before generalising.

The use of the Poisson distribution as an approximation to the binomial distribution.

Use the to connect the rule to the data and decision in the question.

This matters because the determines what can be inferred or chosen; begin with the stated conditions and keep the conclusion tied to the evidence.

Example: apply the to one small, clearly defined case, show the key step or comparison, and explain the result in words.

Boundary: The is not a universal recommendation. Check the syllabus scope, assumptions, units and the limits of the evidence before generalising.