Lesson 6

Dummy Variables in Log-Dependent Models

Big question

How do we interpret a dummy coefficient when the dependent variable is logged?

Lesson progress

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Big question
Concept
Activity
Quiz

Learning objectives

  • Explain dummy variables in log-dependent models in plain language.
  • Use interaction term correctly in an interpretation.
  • Connect the lesson idea to a formula, graph, Python result, or real example.

Simple explanation

The quick percent interpretation is 100 times the coefficient, but the exact conversion uses the exponential function.

Key terms

Interaction term
A regressor formed by multiplying variables so one effect can depend on another variable.
Self-selection
A situation where people choose treatment or participation based on factors related to outcomes.
Dummy variable
A binary regressor used to represent qualitative information in a regression.

Core formula

100[exp⁡(δ)−1]100[\exp(\delta)-1]

Use plain-language interpretation before algebra.

Example

MODULE7_WAGE_GROUPS_SYNTHETIC is an original Ceteris Lab synthetic teaching dataset for dummy variables in log-dependent models. It lets students practice log dummy percent without presenting fabricated real-world empirical findings.

Interactive visual

DummyPercentEffectCalculator: use the controls to code a group, choose a base group, and write one correct interpretation.

Original Module 7 visual for Dummy Variables in Log-Dependent Models.

wage_sample.csv

y variable

wage

The dependent variable. It is the outcome students want to explain.

x variable

education

The explanatory variable. It is used to describe changes in wage.

Live Python

Dummy Variables in Log-Dependent Models Python example

Dummy Variables in Log-Dependent Models Python example

Stdout

Run Python to see results here.

Status / stderr

Ready to run Python in your browser.

Line-by-line guide

  1. Line 1Load a Python library needed for data work or regression.
  2. Line 2Load a Python library needed for data work or regression.
  3. Line 3Load a Python library needed for data work or regression.
  4. Line 5Load the dataset into a pandas DataFrame.
  5. Line 6Add an intercept column to the regression design matrix.
  6. Line 7Create or update a Python object used in the analysis.
  7. Line 8Display a result so students can inspect the output.
  8. Line 9Display a result so students can inspect the output.

Python walkthrough

  1. 1Load pandas and statsmodels so the workflow is reproducible.
  2. 2Read the installed Ceteris Lab synthetic CSV from the public data folder.
  3. 3Create or inspect dummy variables before estimating the model.
  4. 4Estimate OLS with an intercept and the selected regressors.
  5. 5Print coefficients or summaries, then interpret them as associations unless the design supports causality.

Live notebook

Run this lesson as a notebook

Open an editable notebook cell-by-cell, run Python in the browser, and download the `.ipynb` file for later.

Open live notebookDownload .ipynbDownload .py

Related dataset

MODULE7_WAGE_GROUPS_SYNTHETIC

Estimated time

25 to 40 min

Packages

pandas, numpy, statsmodels, patsy

Expected output

Printed Python results that can be compared with the lesson explanation.

Learning goals

  • Load and inspect MODULE7_WAGE_GROUPS_SYNTHETIC.
  • Run the Python cells connected to Dummy Variables in Log-Dependent Models.
  • Interpret the output using dummy variables and qualitative information.

Common errors

  • File not found: check that module7_wage_groups_synthetic.csv is installed or use the course data folder.
  • Package import error: use the browser notebook first, then download for local Jupyter if your local packages differ.
  • Column name error: compare your variable names with the dataset variables listed for this notebook.

Dataset path helper

import pandas as pd

df = pd.read_csv("/data/module-7/module7_wage_groups_synthetic.csv")
df.head()

Interactive activity

DummyPercentEffectCalculator

Dummy Variables in Log-Dependent Models

DummyPercentEffectCalculator: choose the coding rule, base group, or probability interpretation before reading the coefficient.

MODULE7_WAGE_GROUPS_SYNTHETIC

Inputs

Try it yourself

Write one plain-English sentence explaining the main idea from this lesson.

Common mistakes

Check these before you move on.

A regression coefficient describes a pattern unless the assumptions or research design support a causal interpretation.

Quick quiz

What should students check before trusting the result in Dummy Variables in Log-Dependent Models?

Quick quiz

What should students check before trusting the result in Dummy Variables in Log-Dependent Models?

Quick quiz

Why is MODULE7_DISCRETE_OUTCOME_SYNTHETIC a reasonable practice dataset here?

Key takeaway

Dummy Variables in Log-Dependent Models helps students convert qualitative information into transparent, testable regression comparisons.

Download dataDownload resultsDownload interpretation