What is a zero-inflated variable?

What is a zero-inflated variable?

In statistics, a zero-inflated model is a statistical model based on a zero-inflated probability distribution, i.e. a distribution that allows for frequent zero-valued observations.

What is zero-inflated negative binomial model?

The zero-inflated negative binomial (ZINB) regression is used for count data that exhibit overdispersion and excess zeros. The data distribution combines the negative binomial distribution and the logit distribution. The possible values of Y are the nonnegative integers: 0, 1, 2, 3, and so on.

What is Overdispersion in statistics?

Overdispersion describes the observation that variation is higher than would be expected. Some distributions do not have a parameter to fit variability of the observation.

What is the zero-inflated Poisson probability mass function?

The zero-inflated Poisson probability mass function (with zero-inflation parameter 0 ⩽ π ⩽ 1) is: ( − λ) for x > 0. MLE of zero-inflated Poisson data: Suppose we have a sample of n IID data values from this distribution.

Should I use Poisson or binomial or NB regression models?

If you use a standard Poisson or Binomial or NB regression model on such data sets, it can fit badly and will generate poor quality predictions, no matter how much you tweak its parameters.

What is the coefficient of log-likelihood for the children model?

The coefficient for CHILDREN is negative (CHILDREN -1.0810), meaning that as the number of children in the camping group goes up, the number of fish caught by that group goes down! The Maximized Log-Likelihood of this model is -566.43.

Are the coefficients of this regression statistically significant at 99% confidence?

We can see that the coefficients for all 5 regression variables are statistically significant at a 99% confidence level, as evidenced by their p value which is less than 0.01. In fact, the p value is less than 0.001 for all 5 variables, hence it is showing up as 0.000.