Pages in this set

Page 1

Preview of page 1
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.

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

Page 2

Preview of page 2
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…

Page 3

Preview of page 3
Section 4: Normal Distribution
X~N (, 2 )

ALWAYS standardise "x" by converting it to z.







HYPOTHESIS TEST:
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…

Page 4

Preview of page 4



Section 5: Approximations


The distribtuion you start with:





©nefisamarium-meis2

Comments

abi-wan-kenobi

Report

<

abi-wan-kenobi

Report

<3

claudia

Report

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

Similar Mathematics resources:

See all Mathematics resources »See all resources »