Extracting parameter estimates
Coefficient estimates are generally of main interest in a regression model. In the previous exercise you learned how to view the results of the model fit and hence the coefficient values along with their corresponding statistics. In this exercise you will learn how to extract the coefficients from the model object.
The attribute .params contains the coefficients of the fitted model, starting with the intercept value. To compute a 95% confidence interval for the coefficients you can use the method .conf_int() of the fitted model wells_fit.
Recall that the model you fitted was saved as wells_fit and as such is loaded in your workspace.
This exercise is part of the course
Generalized Linear Models in Python
Exercise instructions
- Save the coefficients as
interceptandslopeusing the.paramsattribute. - Print the saved intercept and slope.
- Extract and print 95% confidence intervals for the coefficients using
.conf_int().
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Extract coefficients from the fitted model wells_fit
intercept, slope = wells_fit.____
# Print coefficients
print('Intercept =', ____)
print('Slope =', ____)
# Extract and print confidence intervals
____(____.____)