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

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Linear, Multiple Regression Interview Questions Set 4
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Better luck next time!!