Lesson 11
Interpreting regression output
Big question
Which output numbers matter first, and how should students explain them?
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Big question
Concept
Activity
Quiz
Learning objectives
- Explain interpreting regression output in plain language.
- Use the main concept correctly.
- Connect the lesson idea to a formula, graph, Python result, or real example.
Simple explanation
Which output numbers matter first, and how should students explain them?
Key terms
Example
Which output numbers matter first, and how should students explain them?
Python walkthrough
Interactive activity
Regression output explainer
Read the output one row at a time, then translate it into plain English.
Output itemExampleMeaning
Coefficient2.33The estimated slope on education.
Standard error0.16A measure of uncertainty around the slope estimate.
t-statistic14.8The coefficient divided by its standard error.
p-value< 0.001Evidence against the zero-slope benchmark in this sample.
R-squared0.949The model explains about 94.9% of wage variation in this small sample.
Observations12The number of rows used in the regression.
A precise coefficient and high R-squared still do not automatically prove causality.
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 main idea of Interpreting regression output?
Key takeaway
Use this lesson idea in a cautious, evidence-based regression interpretation.