Lesson 20

Module 7 Capstone

Big question

How do we build a complete qualitative-variable analysis?

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Big question
Concept
Activity
Quiz

Learning objectives

  • Explain module 7 capstone in plain language.
  • Use linear probability model correctly in an interpretation.
  • Connect the lesson idea to a formula, graph, Python result, or real example.

Simple explanation

A complete analysis codes categories, chooses a base group, estimates models, tests differences, checks LPM limits, and reports uncertainty.

Key terms

Linear probability model
An OLS model with a binary dependent variable interpreted as a probability.
Categorical variable
A variable whose values name groups rather than measure amounts.
Dummy-variable trap
Perfect collinearity caused by including an intercept and every category indicator.

Core formula

analysis=coding+model+tests+limits\text{analysis} = \text{coding} + \text{model} + \text{tests} + \text{limits}

Use plain-language interpretation before algebra.

Example

MODULE7_STUDENT_COMPLETION_SYNTHETIC is an original Ceteris Lab synthetic teaching dataset for module 7 capstone. It lets students practice capstone workflow without presenting fabricated real-world empirical findings.

Interactive visual

Module7CapstoneWorkspace: use the controls to code a group, choose a base group, and write one correct interpretation.

Original Module 7 visual for Module 7 Capstone.

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

Module 7 Capstone Python example

Module 7 Capstone 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 3Create or update a Python object used in the analysis.
  3. Line 4Run this Python instruction as part of the lesson workflow.
  4. Line 5Run this Python instruction as part of the lesson workflow.
  5. Line 6Run this Python instruction as part of the lesson workflow.
  6. Line 7Display 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_STUDENT_COMPLETION_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_STUDENT_COMPLETION_SYNTHETIC.
  • Run the Python cells connected to Module 7 Capstone.
  • Interpret the output using dummy variables and qualitative information.

Common errors

  • File not found: check that module7_student_completion_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_student_completion_synthetic.csv")
df.head()

Interactive activity

Module7CapstoneWorkspace

Module 7 Capstone

Module7CapstoneWorkspace: choose the coding rule, base group, or probability interpretation before reading the coefficient.

MODULE7_STUDENT_COMPLETION_SYNTHETIC

Inputs

Dummy value

Try it yourself

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

Common mistakes

Check these before you move on.

Return to the lesson assumptions, units, diagnostics, and source evidence to replace this shortcut with a defensible interpretation.

Quick quiz

What should students check before trusting the result in Module 7 Capstone?

Quick quiz

What should students check before trusting the result in Module 7 Capstone?

Quick quiz

Why is MODULE7_WAGE_GROUPS_SYNTHETIC a reasonable practice dataset here?

Key takeaway

Module 7 Capstone helps students convert qualitative information into transparent, testable regression comparisons.

Download dataDownload resultsDownload interpretation