70 Regression Analysis Interview Questions & Practice Tests

This page lists down practice tests (questions and answers), links to PDF files (consisting of interview questions) on Linear / Logistic Regression for machine learning / data scientist enthusiasts. These questions can prove to be useful, especially for machine learning / data science interns / freshers / beginners to check their knowledge from time-to-time or for upcoming interviews.

Practice Tests on Linear / Multilinear Regression

These are a set of four practice tests (consisting of 40 questions) covering linear (univariate) and multilinear (multivariate) regression in detail.

Some of the following topics have been covered in these tests:

  • Introduction to linear, multilinear regression
  • Linear regression examples
  • Evaluation of linear / multilinear regression models and related techniques including t-test and ANOVA F-tests
  • Sum of squares calculations
  • Topics such as coefficient of determination, pearson correlation coefficient



Practice Tests on Logistic Regression

These are a set of three practice tests (consisting of 30 questions) covering logistic regression in detail.

Some of the following topics have been covered in these tests:

  • Introduction to logistic regression
  • Evaluation of logistic regression model and related techniques such as AIC, Deviance (Null and Residual), ROC curve etc.

Linear, Logistic Regression Interview Questions (PDF)

 

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.

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