Lesson 16
Module 5 Applied Project
Big question
Do large samples save regression inference?
Lesson progress
Complete checkpoints as you learn
Learning objectives
- Explain module 5 applied project in plain language.
- Use asymptotic standard error correctly in an interpretation.
- Connect the lesson idea to a formula, graph, Python result, or real example.
Simple explanation
The applied project asks students to compare what larger samples improve and what they cannot repair.
Key terms
- asymptotic standard error
- A standard error justified by large-sample theory.
- LM test
- A large-sample test based on restricted residuals and an auxiliary regression.
- asymptotic properties
- Properties defined by what happens as sample size grows.
Core formula
Use plain-language interpretation before algebra.
Example
WAGE1 gives students a real-data setting for module 5 applied project. The lesson reports code and diagnostics only after the student runs the live Python lab.
Interactive visual
Module5ProjectWorkspace
Original Module 5 visual for Module 5 Applied Project.
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 5 Applied Project Python example
Module 5 Applied Project Python example
Stdout
Run Python to see results here.
Status / stderr
Ready to run Python in your browser.
Line-by-line guide
- Line 1Load a Python library needed for data work or regression.
- Line 3Create or update a Python object used in the analysis.
- Line 4Run this Python instruction as part of the lesson workflow.
- Line 5Run this Python instruction as part of the lesson workflow.
- Line 6Run this Python instruction as part of the lesson workflow.
- Line 7Display a result so students can inspect the output.
- Line 8Display a result so students can inspect the output.
Python walkthrough
- 1Load libraries and data or set a simulation seed.
- 2Build the model or simulation that matches the lesson question.
- 3Compute the statistic, graph, or summary table.
- 4Interpret the result as large-sample evidence, not automatic 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.
Related dataset
WAGE1
Estimated time
35 to 55 min
Packages
pandas, numpy, scipy
Expected output
Simulation output showing how estimates or test statistics behave as sample size changes.
Learning goals
- Load and inspect WAGE1.
- Run the Python cells connected to Module 5 Applied Project.
- Interpret the output using applied project and asymptotics.
Common errors
- File not found: check that WAGE1.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-5/WAGE1.csv")
df.head()Interactive activity
Module5ProjectWorkspace
Build the applied project plan
Check off simulation, real-data diagnostics, LM testing, and interpretation limits.
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 the Module 5 applied project avoid claiming?
Quick quiz
Which reporting habit is most important in Module 5 Applied Project?
Quick quiz
Why is 401K a reasonable practice dataset here?
Key takeaway
Module 5 Applied Project helps students separate large-sample approximation from valid research design.