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Linear, Multiple Regression Interview Questions Set 4

Logistic regression quiz question and answers

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

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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:

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