Chapter 6 – 50 Most Asked Linear Regression Interview Questions
Topic – 50 Most Asked Linear Regression Interview Questions
Welcome to the 2200 questions series from The Data Monk, in this series we will cover all the topics in a Question-Answer mode that are required for anyone who wants to make a career in the following field:-
– Data Analysis
– Business Analysis
– Business Intelligence Engineering
– Machine Learning
– Data Science
– Product Analysis
– Data Engineering
– Risk Analysis
These 2200 questions are useful for anyone who is in their 2nd-3rd year of engineering to 8-10 years of experience in the IT industry( be it QA/Development/Support) and are willing to make a career in Analytics.
Most Asked Linear Regression Interview Questions
Why Analytics is a domain for you?
If you want to make a handsome switch with a good package then Analytics is for you because of the following reasons:-
– It is a high-paying job
– It is interesting as you will have a good impact on the growth of the organization
– It involves a lot of things like requirement gathering, building logic, making ETL, pipeline creation, reporting to the CXOs, and so on. So, it is a very impactful role
– It has a HUGE demand in the future as the data will keep on growing and so will your role
How much does an analytics role pay?
The CTC of the role will definitely depend on multiple factors but just to give you a glimpse of it:-
“Anyone from a tier 2-3 college with good knowledge of the material that we are providing will have a fair chance to bag something like 15+ LPA for a fresher. The more you grind the better you get and the CTC grows with experience.”
Now coming back to why you should try The Data Monk for your Analytics journey.
Why The Data Monk?
We are a group of 30+ Analytics Engineers working in various product-based companies like Zomato, Ola, OYO, Google, Rapido, Uber, Ugam, BYJUs, etc. and we observed that people do not have a well-structured way to enhance their knowledge. There are multiple courses here and there, but no one has consolidated what needs to be learned in order to move to the analytics domain.
Further, there are courses from Large institutes where they charge you something like 2-5 lacks and try to teach you everything from Data structure to SQL to Power BI to ML. You do not have to spend so much on these topics.
We followed a very old-school way, take a topic and solve 100-200 questions on these topics. Learn them, understand them, and revise them. This should be enough for you to crack that domain.
For example, if I am a very beginner in SQL, then I will just try to solve 200 questions starting from the definition to advance level questions. After solving and revising these questions I should have a good amount of knowledge to answer 6 out of 10 questions asked in an interview and going by that calculation I can be a strong candidate in 5-7 out of 10 companies.
See, by the end, you need to convert a job first and then keep on learning in the organization.
Most of the books are on questions like ‘250 questions to crack SQL interview’ and this will cost you around 250 rupees, take the book, understand, and learn it. This small amount can bag you a 15 LPA job 🙂
You can trust us as we have guided more than 1000 people to make a career in Analytics
2200 Analytics Interview Questions
Coming back to the topic, below is the list of 250 SQL questions to Ace any Analytics Interview
Chapter 1 – SQL – 250 SQL questions to Ace any Analytics Interview
Chapter 2 – Python – 200 Most Asked Python Interview Questions
Chapter 3 – Pandas – 100 Most Asked Pandas Interview Questions with Solution
Chapter 4 – Numpy – 100 Most Asked Numpy Interview Questions with solution
Chapter 5 – Case Study and Guesstimate – 100 Case Study and Guesstimate with a complete solution
Chapter 6 -Linear Regression – 50 Most Asked Linear Regression Interview Questions with solution
Chapter 7 – Logistic Regression – 50 Most Asked Logistic Regression Interview Questions with solution
750. What is Linear Regression?
751. Why is linear regression important?
752. What are some common places where Linear Regression is used?
753.What is the Equation of Linear Regression?
754. What are Independent(X) and Dependent variables(Y)?
755. What is β0 OR Y Intercept?
756. What is β1 OR Slope coefficient?
757. What is ε OR Error Term OR Residual?
758. What is correlation? (V.V.I.)
759. What is Positive Correlation?
760. What is Negative Correlation?
761. What is a Simple linear regression?
762. What is a Multiple linear regression?
763. What are the advantages of Linear Regression?
764. What are the disadvantages of Linear Regression?
765. What is Overfitting?
766. How to deal with overfitting in Linear Regression?
767. What is Underfitting?
768. What is regularisation?
769. What is Feature Selection?
770. What are the type of feature selection?
771. What are the assumptions of Linear Regression?
772. When to drop an outlier and when not to drop an outlier?
773. What is the assumption of Linearity?
774. What is the assumption of multicollinearity?
775. What are the functions used to check multicollinearity in python?
776. What is corr() function?
777. What is vif?
778. How to deal with the problem of multicollinearity?
779.What is R square?
780. What is Adjusted R square?
781. Difference between R2 and Adjusted R2?
782. What is RMSE?
783. Difference between Correlation and Regression?
784. Why do we split our data into training and testing?
785. Steps for performing Linear Regression in Python.
786. How does multicollinearity affect the linear regression?
787. Can you name a possible method of improving the accuracy of a linear regression model?
788. What are outliers? How do you detect and treat them?
789.How do you interpret a Q-Q plot in a linear regression model?
790. What is the importance of the F-test in a linear model?
791. What are the disadvantages of the linear regression model?
792. What is the curse of dimensionality? Can you give an example?
793. Compare Linear Regression and Decision Tree
794. Name a disadvantage of R-squared and explain how would you address it?
794. Does correlation imply causation? Why or why not?
795. Is linear regression suitable for time series analysis?
796. How do you determine if a linear regression model is a good fit for your data?
797. What are some common techniques for dealing with multicollinearity in multiple linear regression models?
798. How do you check for heteroscedasticity in a linear regression model, and what are some techniques for addressing it?
799. How do you evaluate the performance of a linear regression model, and what metrics do you use?
800. How do you handle outliers and influential points in a linear regression model?
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