Linear, Multiple Regression Interview Questions Set 4

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 some of the following concepts related with linear regression models:

  • Degrees of freedom
  • Concepts related with Sum of Squares Total (SST), Sum of Squares Regression (SSR) and Sum of Squares Error (SSE)

Linear Regression – Measures of Variation

The diagram below represents measures of variation in terms of following:

  • Actual value of dependent variable
  • Predicted value of dependent variable (best fit line)
  • Average value of dependent variable assuming the value of all coefficients is zero

linear regression measures of variation

Pay attention to some of the following in above diagram:

  • Sum of squares total (SST) is sum of squares of actual value (Y) minus the average value of dependent variable (Y bar)
  • Sum of squares total (SSE) is sum of squares of actual value (Y) minus the predicted value as per the best fit line (Y^)
  • Sum of squares total (SSR) is sum of squares of predicted value as per the best fit line (Y^) minus average value of dependent variable (Y bar)
SST = SSR (variability due to regression) + SSE (Unexplained variability)
Value of R-Squared = SSR / SST

The objective is to maximise SSR and minimize SSE.

Other tests in this series includes some of the following:

Practice Test

[wp_quiz id=”5793″]

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:


Latest posts by Ajitesh Kumar (see all)
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 are AI Agents? How do they work?

Artificial Intelligence (AI) agents have started becoming an integral part of our lives. Imagine asking…

2 weeks ago

Agentic AI Design Patterns Examples

In the ever-evolving landscape of agentic AI workflows and applications, understanding and leveraging design patterns…

2 weeks ago

List of Agentic AI Resources, Papers, Courses

In this blog, I aim to provide a comprehensive list of valuable resources for learning…

2 weeks ago

Understanding FAR, FRR, and EER in Auth Systems

Have you ever wondered how systems determine whether to grant or deny access, and how…

3 weeks ago

Top 10 Gartner Technology Trends for 2025

What revolutionary technologies and industries will define the future of business in 2025? As we…

3 weeks ago

OpenAI GPT Models in 2024: What’s in it for Data Scientists

For data scientists and machine learning researchers, 2024 has been a landmark year in AI…

3 weeks ago