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.

This test primarily focus on following two concepts related with evaluation of linear regression models:

- Coefficient of determination (R-squared)
- Pearson correlation coefficient

Note that the above two concepts are used to understand some of the following:

- Impact of one or more parameters on the dependent variable in positive or negative manner
- Overall impact/performance of regression model based on addition of one or more independent variables

Other tests in the series includes some of the following:

#### Which of the following can be used to understand the positive or negative relationship between dependent and independent variables

#### The goal of the regression model is to achieve the R-squared value ________

#### Pearson correlation coefficient is __________ to coefficient of determination

#### Pearson correlation coefficient does always have positive value

#### Value of Pearson correlation coefficient near to zero represents the fact there is a stronger relationship between dependent and independent variables

#### Population correlation coefficient and sample correlation coefficient are one and the same

#### The value of Pearson correlation coefficient falls in the range of _________

#### The value of correlation coefficient and R-squared remains same for all samples of data

#### The large value of R-squared can be safely interpreted as the fact that estimated regression line fits the data well.

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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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