Lesson 2

What Heteroskedasticity Does and Does Not Break

Big question

Which OLS claims survive changing error variance, and which inference claims need repair?

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Big question
Concept
Activity
Quiz

Learning objectives

  • Explain what heteroskedasticity does and does not break in plain language.
  • Use homoskedasticity correctly in an interpretation.
  • Connect the lesson idea to a formula, graph, Python result, or real example.

Simple explanation

Heteroskedasticity is about changing uncertainty. In what heteroskedasticity does and does not break, students focus on coefficient validity versus inference validity and learn how to describe that pattern without confusing it with coefficient bias.

Key terms

Homoskedasticity
A constant conditional error variance assumption used by conventional OLS standard errors.
Breusch-Pagan test
A diagnostic test that regresses squared residuals on variables thought to explain error variance.
Feasible GLS
A weighted estimator that first estimates the variance function and then uses predicted weights.

Core formula

E(ui∣Xi)=0,butVar(ui∣Xi)mayvaryE(u_i | X_i)=0, but Var(u_i | X_i) may vary

Use plain-language interpretation before algebra.

Example

MODULE8_ROBUST_SE_DEMO supports original Ceteris Lab practice for what heteroskedasticity does and does not break. Synthetic files are clearly labeled as synthetic, and installed course datasets are used only when the file is present.

Interactive visual

ConsequenceClassifier: adjust the controls, classify the diagnostic evidence, and write one sentence explaining the implication for inference.

Original Module 8 visual for What Heteroskedasticity Does and Does Not Break.

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

What Heteroskedasticity Does and Does Not Break Python example

What Heteroskedasticity Does and Does Not Break 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 4Load the dataset into a pandas DataFrame.
  4. Line 5Add an intercept column to the regression design matrix.
  5. Line 6Estimate an ordinary least squares regression.
  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.
  9. Line 10Display a result so students can inspect the output.
  10. Line 11Display a result so students can inspect the output.

Python walkthrough

  1. 1Load the Python packages needed for data, regression, diagnostics, or plotting.
  2. 2Read an installed Ceteris Lab dataset from a browser-safe public path.
  3. 3Estimate the baseline model before changing the covariance method or weights.
  4. 4Print diagnostic evidence or a coefficient comparison so students can inspect the result.
  5. 5Interpret the output as practice evidence and avoid making real empirical claims from synthetic data.

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

MODULE8_ROBUST_SE_DEMO

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 MODULE8_ROBUST_SE_DEMO.
  • Run the Python cells connected to Robust Standard Errors.
  • Interpret the output using heteroskedasticity and robust standard errors.

Common errors

  • File not found: check that module8_robust_se_demo.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-8/module8_robust_se_demo.csv")
df.head()

Interactive activity

Consequence Classifier

What Heteroskedasticity Does and Does Not Break

change the inputs, inspect the feedback, and decide whether robust inference, diagnostics, WLS, or reporting caution is needed.

MODULE8_ROBUST_SE_DEMO

Inputs

Choose a report ingredient

Visual preview

Changing variance across fitted values

fitted valueresidual spread

A wider fan means the uncertainty changes across observations. Robust standard errors adjust inference; WLS needs a defensible variance model.

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 a careful Module 8 report include for What Heteroskedasticity Does and Does Not Break?

Quick quiz

What should a careful Module 8 report include for What Heteroskedasticity Does and Does Not Break?

Quick quiz

Why is MODULE8_HOUSING_VARIANCE_SYNTHETIC a reasonable practice dataset here?

Key takeaway

What Heteroskedasticity Does and Does Not Break helps students diagnose changing variance and choose inference or weighting methods without overclaiming what those methods can fix.

Download dataDownload resultsDownload interpretation