Skip to content

From Predictions to Explanations

Agenda

  • Theory
    • Presentation & Discussion
  • Code
    • Modeling & Exporting Regression Tables
  • Application
    • Interpreting Regression Coefficients

Theory

Code

We will build a regression model to examine how political knowledge affects affective polarization. We will also learn how to export the regression results to Markdown (& LaTeX).

Building the Regression Model

We will use ordinary least squares (OLS) regression to model the relationship between affective polarization and political knowledge.

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

df = pd.read_csv("../data/clean_anes.csv")

# Define the regression formula
formula = "affective_polarization ~ political_knowledge_scale + age + sex + education + ideology"

# Fit the regression model
model = sm.ols(formula=formula, data=df).fit()
print(model.summary())

Exporting to Mardown or LaTeX

We can use statsmodels to export our regression table to LaTeX format.

from statsmodels.iolib.summary2 import summary_col

latex_output = summary_col([model]).as_latex()
print(latex_output)

This output can be directly included in your academic papers written in markdown to present the regression results clearly and professionally.

Application

Let's head to Github and open our codespace (text editor)

What's Next?