Lesson 20

Reporting Regression Results Professionally

Big question

How do we turn inference into a professional report?

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Big question
Concept
Activity
Quiz

Learning objectives

  • Explain reporting regression results professionally in plain language.
  • Use f statistic correctly in an interpretation.
  • Connect the lesson idea to a formula, graph, Python result, or real example.

Simple explanation

A professional report states the hypothesis, model, coefficient magnitude, uncertainty, fit, sample size, and limitations.

Key terms

F statistic
A statistic used to test several linear restrictions jointly.
joint significance
Evidence that a group of variables matters collectively.
general linear restriction
A set of linear equations imposed on regression parameters.

Core formula

report=estimate+uncertainty+interpretation+limitationreport = estimate + uncertainty + interpretation + limitation

Use plain-language interpretation before algebra.

Example

CEOSAL2 gives students a real-data setting for reporting regression results professionally. The lesson emphasizes inference mechanics and interpretation, not memorized output.

Interactive visual

RegressionReportBuilder

Original Module 4 visual for Reporting Regression Results Professionally.

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

Reporting Regression Results Professionally Python example

Reporting Regression Results Professionally 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 6Create or update a Python object used in the analysis.
  6. Line 7Add an intercept column to the regression design matrix.
  7. Line 8Estimate an ordinary least squares regression.
  8. Line 9Create or update a Python object used in the analysis.
  9. Line 10Create or update a Python object used in the analysis.
  10. Line 11Create or update a Python object used in the analysis.
  11. Line 12Create or update a Python object used in the analysis.
  12. Line 13Create or update a Python object used in the analysis.
  13. Line 14Create or update a Python object used in the analysis.
  14. Line 15Create or update a Python object used in the analysis.
  15. Line 16Create or update a Python object used in the analysis.
  16. Line 17Run this Python instruction as part of the lesson workflow.
  17. Line 18Create or update a Python object used in the analysis.
  18. Line 19Display a result so students can inspect the output.
  19. Line 20Display a result so students can inspect the output.
  20. Line 21Display a result so students can inspect the output.

Python walkthrough

  1. 1Load the real dataset and keep the variables needed for the model.
  2. 2Estimate OLS with statsmodels.
  3. 3Compute the statistic, p-value, or confidence interval.
  4. 4Interpret the result with units, controls, and limitations.

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

CEOSAL2

Estimated time

25 to 40 min

Packages

pandas, numpy, statsmodels, patsy

Expected output

A regression or inference table with coefficients, uncertainty, and short interpretation notes.

Learning goals

  • Load and inspect CEOSAL2.
  • Run the Python cells connected to Reporting Regression Results Professionally.
  • Interpret the output using reporting and regression table.

Common errors

  • File not found: check that CEOSAL2.DTA 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_stata("/data/CEOSAL2.DTA")
df.head()

Interactive activity

RegressionReportBuilder

Draft a regression report

Assemble estimate, uncertainty, p-value, magnitude, and limitation.

CEOSAL2

Inputs

Pick the next report ingredient

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 is the safest inference focus in Reporting Regression Results Professionally?

Quick quiz

Which reporting habit is most important in Reporting Regression Results Professionally?

Quick quiz

Why is RENTAL a reasonable practice dataset here?

Key takeaway

Reporting Regression Results Professionally turns regression output into evidence only when the hypothesis, assumptions, and magnitude are stated clearly.

Download dataDownload resultsDownload interpretation