Regression Table

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

The content of the table includes:

• Information about the model
• Coefficients of the linear regression function
• Regression statistics
• Statistics of the coefficients from the linear regression function
• Other information that we will not cover in this module

Regression Table with Average_Pulse as Explanatory Variable

You can now begin your journey on analyzing advanced output!

Create a Linear Regression Table in Python

Here is how to create 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. Statsmodels is a statistical library in Python.
• Use the full_health_data set.
• Create a model based on Ordinary Least Squares with smf.ols(). Notice that the explanatory variable must be written first in the parenthesis. Use the full_health_data data set.
• By calling .fit(), you obtain the variable results. This holds a lot of information about the regression model.
• Call summary() to get the table with the results of linear regression.