Visualization
Now let us go into the visualization of the studentized residual. With the help of matplotlib we can make a plot of the predictor variable values VS the corresponding studentized residuals.
Example:
Python3
# Python program to draw the plot# of stundenterized residual# Importing necessary packagesimport numpy as npimport pandas as pdimport statsmodels.api as smfrom statsmodels.formula.api import olsimport matplotlib.pyplot as plt# Creating dataframedataframe = pd.DataFrame({'Score': [80, 95, 80, 78, 84, 96, 86, 75, 97, 89], 'Benchmark': [27, 28, 18, 18, 29, 30, 25, 25, 24, 29]})# Building simple linear regression modelsimple_regression_model = ols('Score ~ Benchmark', data=dataframe).fit()# Producing studentized residualresult = simple_regression_model.outlier_test()# Defining predictor variable values and # studentized residualsx = dataframe['Score']y = result['student_resid']# Creating a scatterplot of predictor variable # vs studentized residualsplt.scatter(x, y)plt.axhline(y=0, color='black', linestyle='--')plt.xlabel('Points')plt.ylabel('Studentized Residuals')# Save the plotplt.savefig("Plot.png") |
Output:
Plot.png:
How to Calculate Studentized Residuals in Python?
Studentized residual is a statistical term and it is defined as the quotient obtained by dividing a residual by its estimated standard deviation. This is a crucial technique used in the detection of outlines. Practically, one can claim that any type of observation in a dataset having a studentized residual of more than 3 (absolute value) is an outlier.
The following Python libraries should already be installed in our system:
- pandas
- numpy
- statsmodels
You can install these packages on your system by using the below command on the terminal.
pip3 install pandas numpy statsmodels matplotlib
Contact Us