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What is the appropriate distribution for count data?
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.
What is a count response variable?
Identified count response variables in informetric studies include the number of authors, the number of references, the number of views, the number of downloads, and the number of citations received by an article. Also of a count nature are the number of links from and to a website.
Which is an example of a count response variable?
An example of a regression model with a count response variable is the prediction of the number of times a person perpetrated domestic violence against his or her partner in the last year based on whether he or she had witnessed domestic violence as a child and who the perpetrator of that violence was.
Can a transformation of a continuous response variable produce normal errors?
To meet this assumption when a continuous response variable is skewed, a transformation of the response variable can produce errors that are approximately normal. Often, however, the response variable of interest is categorical or discrete, not continuous.
Are there any problems with linear regression for count data?
The distribution of counts is discrete, not continuous, and is limited to non-negative values. There are two problems with applying an ordinary linear regression model to these data. First, many distributions of count data are positively skewed with many observations in the data set having a value of 0.
Can a regression model produce negative predicted values?
Second, it is quite likely that the regression model will produce negative predicted values, which are theoretically impossible.