Fitting negative binomial
The negative binomial allows for the variance to exceed the mean, which is what you have measured in the previous exercise in your data crab. In this exercise you will recall the previous fit of the Poisson regression using the log link function and additionally fit negative binomial model also using the log link function.
You will analyze and see how the statistical measures were changed.
The model crab_pois and crab is loaded in your workspace.
Questo esercizio fa parte del corso
Generalized Linear Models in Python
Istruzioni dell'esercizio
- Define a formula for the regression model so that
satis predicted bywidth. - Fit the negative binomial using
NegativeBinomial()and save the model ascrab_NB. - Print the summaries of the Poisson model
crab_poisand the newly fitted negative binomial model.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Define the formula for the model fit
formula = '____ ~ ____'
# Fit the GLM negative binomial model using log link function
crab_NB = smf.glm(formula = ____, data = ____,
family = ____.____.____).____
# Print Poisson model's summary
print(____.____)
# Print the negative binomial model's summary
print(____.____)