I'm hoping someone can provide an intuitive overview of what quasibinomial is and what it does. I'm particularly interested in these points:

1. How quasibinomial differs to the binomial distribution

2. When the response variable is a proportion (example values include 0.23, 0.11, 0.78, 0.98), a quasibinomial model will run in R but a binomial model will not.

3. Why quasibinomial models should be used when a TRUE/FALSE response variable is overdispersed.

I'm hoping someone can provide an intuitive overview of what quasibinomial distribution is and what it does. I'm particularly interested in these points:

1. How quasibinomial differs to the binomial distribution.

2. When the response variable is a proportion (example values include 0.23, 0.11, 0.78, 0.98), a quasibinomial model will run in R but a binomial model will not.

3. Why quasibinomial models should be used when a TRUE/FALSE response variable is overdispersed.

I'm hoping someone can provide an intuitive overview of what quasibinomial is and what it does. I'm particularly interested in these points:

1. How quasibinomial differs to the binomial distribution

2. When the response variable is a proportion (example values include 0.23, 0.11, 078, 0.98), a quasibinomial model will run in R but a binomial model will not

3. Why quasibinomial should be used when a TRUE/FALSE response variable is overdispersed

I'm hoping someone can provide an intuitive overview of what quasibinomial is and what it does. I'm particularly interested in these points:

1. How quasibinomial differs to the binomial distribution

2. When the response variable is a proportion (example values include 0.23, 0.11, 0.78, 0.98), a quasibinomial model will run in R but a binomial model will not.

3. Why quasibinomial models should be used when a TRUE/FALSE response variable is overdispersed.