Count data
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In
Count variables
An individual piece of count data is often termed a count variable. When such a variable is treated as a random variable, the Poisson, binomial and negative binomial distributions are commonly used to represent its distribution.
Graphical examination
Graphical examination of count data may be aided by the use of data transformations chosen to have the property of stabilising the sample variance. In particular, the square root transformation might be used when data can be approximated by a Poisson distribution (although other transformation have modestly improved properties), while an inverse sine transformation is available when a binomial distribution is preferred.
Relating count data to other variables
Here the count variable would be treated as a
The Poisson distribution can form the basis for some analyses of count data and in this case Poisson regression may be used. This is a special case of the class of generalized linear models which also contains specific forms of model capable of using the binomial distribution (binomial regression, logistic regression) or the negative binomial distribution where the assumptions of the Poisson model are violated, in particular when the range of count values is limited or when overdispersion is present.
See also
- Index of dispersion
- Empirical distribution function
- Frequency distribution
Further reading
This article includes a improve this article by introducing more precise citations. (November 2009) ) |
- ISBN 978-1-107-66727-3.
- ISBN 978-0-521-19815-8.
- Winkelmann, Rainer (2008). Econometric Analysis of Count Data (Fifth ed.). Springer. ISBN 978-3-540-77648-2.