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Section 2: Poisson Distribution

If X~ P ():
Mean () of X = E(X) =
Variance (2) of X = Var(X) =
If mean is close to the variance then Poisson is a suitable model.

Events occur "randomly" (this means that event is not predictable) and
"independently" (this means…

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Section 3: Contingency Tables

row total x column total
Expected frequency = overall total

H0: there is no association between the variables `x' and `y'
H1: there is some association between the variables `x' and `y'
Work out the `contributions' :

Degrees of freedom () = (no. of rows 1…

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Section 4: Normal Distribution
X~N (, 2 )

ALWAYS standardise "x" by converting it to z.

1. State H0 and H1:
Onetailed test H0: = k H1: > k or H1: < k
Two tailed test H0: = k H1: k

2. Define :
Where denotes the mean…

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Section 5: Approximations

The distribtuion you start with:








Thank-you! This is brilliant, you've explained some of those tricky definitions they often ask! :)

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