Question

Overdispersion: (Hint: It is when the observed variance is bigger than expected from the logistic regression model.)

a. Tends to limit standard errors.

b. Doesn’t affect the model parameters (b-values).

c. Biases our conclusions about the significance and population value of the model parameters.

d. All of these are correct.

Answer 1

Solution

option d

all these are correct

the standard errors obtained from the model will be incorrect and may be seriously underestimated and consequently we may incorrectly assess the significance of individual regression parameters. Also, changes in deviance associated with model terms will also be too large and this will lead to the selection of overly complex models. Finally, our interpretation of the model will be incorrect and any predictions will be too precise.