Plotting data and linear model fit
In the previous exercises you have practiced how to fit and interpret the Poisson regression model. In this exercise you will visually analyze the crab
data and then the model fit.
First, you will plot a linear fit to the data, which later on you will use to compare to Poisson regression fitted values.
This exercise is part of the course
Generalized Linear Models in Python
Exercise instructions
- Import
seaborn
andmatplotlib
libraries. - Using
crab
dataset plot the data points withwidth
on the x-axis andsat
on the y-axis, with jitter of0.3
for thesat
variable. - Add a linear model fit by setting the argument
fit_reg
toTrue
. - Set
'color'
of the line fit as'green'
and the'label'
as'LM fit'
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import libraries
import ____ as sns
import ____.pyplot as plt
# Plot the data points and linear model fit
sns.regplot(____, ____, data = ____,
y_jitter = ____,
fit_reg = ____,
line_kws = {'color':____,
'label':____})
# Print plot
plt.show()