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

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

In ANOVA test for regression, degrees of freedom (regression) is _________

In ANOVA test for regression, degrees of freedom (regression) is _________

For SST as sum of squares total, SSE as sum of squared errors and SSR as sum of squares regression, which of the following is correct?

The value of coefficient of determination is which of the following?

Mean squared error can be calculated as _______

Sum of Squares Regression (SSR) is ________

Sum of Squares Error (SSE) is ________

Sum of Squares Total (SST) is ________

______ the value of sum of squares regression (SSR), better the regression model

The objective for regression model is to minimize ______ and maximize ______

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

Ajitesh Kumar

Ajitesh has been recently working in the area of AI and machine learning. Currently, his research area includes Safe & Quality AI. In addition, he is also passionate about various different technologies including programming languages such as Java/JEE, Javascript and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc.

He has also authored the book, Building Web Apps with Spring 5 and Angular.
Ajitesh Kumar

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