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22 Cards in this Set

  • Front
  • Back

Poission Distribution

Log linear therefore no negative values

Assumption of data in Poisson distribution

Small counts

One parameter distribution specifird by the mean ( log linear, Poission)


Therefore variance/mean=1

Infinity poisson distribution?

Mathematically identical to normal distribution

How to write a log linear function


What does a link function do?

Other residual distributions, using likelihood estimation

What is maxium likelihood?

ML instead of Least of squares

Why isnt LS appropriate for non normal errors?

Not for non normal distribution.

LS cause biased estimates for non normal data where ML isn't as its based in Likelihood estimation

What do we assume for binomial, Poisson etc in terms of errors

We use deviances not sum of sqaures

What are is Deviances?

Assumptions about Poisson errors

Probability of the event occuring is random unlike winning a fight or not

However overall distribution might be clumped of aggregated, not completely random

What is HF, scale parameter, Dispersion parameter


Common- influenced by response variable that isn't measured, or non-poisson distribution

What type of errors can overdispersion cause?

H.F>1.5 cause for worry!

Lead to type 1 errors

What are type 1 and 2 errors?

How to fix Overdispersion?

Assumed value of scale parameter =1 to actual value, =residual deviance/d.f. and using F-test

What do if you cant fix overdispersion

Assume negative binomal error stucture

Tranform or use non parametric analyses


Less common

Type 2 Errors

Dealt with Rescaling

How to check for H.F in an anova table?

Difference between negative binomial and poission

When is negative binomal distribution more appropriate than poisson

When distribution is aggregated

How to describe negative binomal distribution?