DS Regression Table

Regression Table

The output of linear regression can be summarized in a regression table.

The table includes:

Information about the model
Coefficients of the linear regression function
Regression statistics
Statistics for the coefficients from the linear regression function
Other details that will not be covered in this module.

Regression Table with Average_Pulse as Explanatory Variable

Create a Linear Regression Table in Python

Here’s how to generate a linear regression table in Python:

Example

import pandas as pd
import statsmodels.formula.api as smf

full_health_data = pd.read_csv(“data.csv”, header=0, sep=“,”)

model = smf.ols(‘Calorie_Burnage ~ Average_Pulse’, data = full_health_data)
results = model.fit()
print(results.summary())

Example Explained:

Import the library statsmodels.formula.api as smf, which is a statistical library in Python.
Use the full_health_data dataset.
Create a model based on Ordinary Least Squares with smf.ols(). Note that the explanatory variable should be listed first in the parentheses. Use the full_health_data dataset.
Call .fit() to obtain the variable results, which contains detailed information about the regression model.
Call summary() to generate the table displaying the results of the linear regression.