Data Science - Regression Table

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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())

Try it Yourself »

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.

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