This page lists down the** practice tests** / **interview questions and answers** 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.

Note that this is a series of tests which represents questions covering following topics:

- Concepts related with simple linear regression and multi-linear regression
- R-squared and Adjusted R-squared
- Tests such as T-test, ANOVA tests for hypothesis testing

Other tests in the series includes some of the following:

#### In _________ regression, there is ________ dependent variable and ________ independent variable(s)

#### In _________ regression, there is ________ dependent variable and ________ independent variable(s)

#### It is OK to add independent variables to a multi-linear regression model as it increases the explained variance of the model and makes model more effcient

#### Linear or multilinear regression helps in predicting _______

#### Regression analysis helps in studying __________ relationship between variables.

#### Regression analysis helps in doing which of the following?

#### The best fit line is achieved by finding values of the parameters which minimizes the sum of __________

#### Best fit line is also termed as _______

#### Which of the following can be used to understand the statistical relationship between dependent and independent variables in linear regression?

#### It is absolutely OK to state that correlation does imply causation

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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:

- Beta Distribution Explained with Python Examples - September 24, 2020
- Bernoulli Distribution Explained with PythonExamples - September 23, 2020
- K-Nearest Neighbors Explained with Python Examples - September 22, 2020