What is a residual in linear regression?
PICTURE THIS: HOW TO EXPLAIN IT
Simple meaning
A residual is actual y minus predicted y for a row.
WHY — Linear Regression instead of guessing?
Why interviewers care about Linear Regression:
people who only read docs from people who shipped.
and tied to AI / ML work.
Name the idea, why it exists, then one short example.
End with when you use it and one common pitfall.
STEPS — What happens step by step?
Before you speak the answer, walk the interviewer through these steps:
- 1A residual is actual
y minus predicted y for a row.
- 2Residual plots show systematic
mistakes the model is making.
- 3Patterns versus predicted values
often reveal nonlinearity or changing variance.
- 4Give an example
One tiny concrete case you can say aloud.
- 5Common mistake
What juniors usually get wrong.
- 6Close
When you pick this over the alternative.
EXAMPLE — See it in action
Here's a short line you can speak, broken into clear beats:
Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.
Key takeaway
A residual is actual y minus predicted y for a row. Residual plots show systematic mistakes the model is making.