The effect of multicollinearity

Using the crab dataset you will analyze the effects of multicollinearity. Recall that multicollinearity can have the following effects:

Coefficient is not significant, but variable is highly correlated with \(y\).
Adding/removing a variable significantly changes coefficients.
Not logical sign of the coefficient.
Variables have high pairwise correlation.

This exercise is part of the course

Generalized Linear Models in Python

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Exercise instructions

Import necessary functions from statsmodels library for GLMs.
Fit a multivariate logistic regression model with weight and width as explanatory variables and y as the response.
View model results using print() function.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import statsmodels
import ____.____ as sm
from ____.____.____ import glm

# Define model formula
formula = '____ ~ ____'

# Fit GLM
model = glm(____, ____ = ____, ____ = sm.____.____).____

# Print model summary
____(____.____)

Edit and Run Code