Easy Linear Regression Question 23 of 223

What assumptions does ordinary linear regression make?

AI & Data Analytics · Speak this in 60–90 seconds · Faridabad & Delhi NCR

PICTURE THIS: HOW TO EXPLAIN IT

IdeaLinear Regression

HowWhat happens inside

Why they askShows real use

Simple meaning

It assumes a roughly linear relationship, independent errors, and fairly constant residual variance.

WHY — Linear Regression instead of guessing?

Why interviewers care about Linear Regression:

Linear Regression questions separate

people who only read docs from people who shipped.

Keep it short, concrete,

and tied to AI / ML work.

Stay structured

Name the idea, why it exists, then one short example.

Close cleanly

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:

1
It assumes a roughly
linear relationship, independent errors, and fairly constant residual variance.
2
Strong multicollinearity among features
also makes coefficients unstable.
3
You check these with
residual plots rather than assuming they hold.
4
Give an example
One tiny concrete case you can say aloud.
5
Common mistake
What juniors usually get wrong.
6
Close
When you pick this over the alternative.

EXAMPLE — See it in action

Here's a short line you can speak, broken into clear beats:

Say this line

“Strong multicollinearity among features also makes coefficients unstable.”

Break into beats

Strongmulticollinearityamongfeaturesalsomakes

Speaking order

2987408337471632900

Note: Adapt this scaffold to your own project — keep it under 60–90 seconds.

Key takeaway

It assumes a roughly linear relationship, independent errors, and fairly constant residual variance. Strong multicollinearity among features also makes coefficients unstable.