Linear, Multiple Regression Interview Questions Set 3

This page lists down the practice tests / interview questions for Linear (Univariate / Simple Linear) / Multiple (Multilinear / Multivariate) regression in machine learning. Those wanting to test their machine learning knowledge in relation with linear/multi-linear regression would find the test useful enough. The goal for these practice tests is to help you check your knowledge in numeric regression machine learning models from time-to-time. More importantly, when you are preparing for interviews, these practice tests are intended to be handy enough. Those going for freshers / intern interviews in the area of machine learning would also find these practice tests / interview questions to be very helpful.

This test primarily focus on following two concepts related with linear regression models:

  • Hypothesis testing (Null hypothesis) vis-a-vis P-value
  • T-tests vis-a-vis ANOVA (Analysis of Variance) F-tests
  • R-squared vs adjusted R-Squared

Note some of the following in relation with tests:

  • Linear association between dependent and independent variables is determined using both T-test and ANOVA F-tests
  • Adjusted R-Squared value is used to determine the impact of introducing new independent variable in the regression model. As like R-squared value which increases with addition of new predictor variables, adjusted R-squared value increased only when there is a positive impact or else the value of adjusted R-squared value decreases.

Other tests in this series includes some of the following:

[wp_quiz id=”5775″]

In case you have not scored good enough, it may be good idea to go through basic machine learning concepts in relation with linear / multi-linear regression. Following is the list of some good courses / pages:


Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.

Recent Posts

What is Embodied AI? Explained with Examples

Artificial Intelligence (AI) has evolved significantly, from its early days of symbolic reasoning to the…

1 month ago

Retrieval Augmented Generation (RAG) & LLM: Examples

Last updated: 25th Jan, 2025 Have you ever wondered how to seamlessly integrate the vast…

4 months ago

How to Setup MEAN App with LangChain.js

Hey there! As I venture into building agentic MEAN apps with LangChain.js, I wanted to…

4 months ago

Build AI Chatbots for SAAS Using LLMs, RAG, Multi-Agent Frameworks

Software-as-a-Service (SaaS) providers have long relied on traditional chatbot solutions like AWS Lex and Google…

4 months ago

Creating a RAG Application Using LangGraph: Example Code

Retrieval-Augmented Generation (RAG) is an innovative generative AI method that combines retrieval-based search with large…

5 months ago

Building a RAG Application with LangChain: Example Code

The combination of Retrieval-Augmented Generation (RAG) and powerful language models enables the development of sophisticated…

5 months ago