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

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:

- Linear, Multiple Regression Interview Questions Set 1
- Linear, Multiple Regression Interview Questions Set 2
- Linear, Multiple Regression Interview Questions Set 3

### Practice Test

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

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

Please select 2 correct answers

#### 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 ______

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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:

- Bernoulli Distribution Explained with PythonExamples - September 23, 2020
- K-Nearest Neighbors Explained with Python Examples - September 22, 2020
- Local & Global Minima Explained with Examples - September 21, 2020