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 two concepts related with linear regression models:

- Hypothesis testing (Null hypothesis) vis-a-vis P-value
- T-tests vis-a-vis ANOVA (Analysis of Variance) F-tests
- R-squared vs adjusted R-Squared

Note some of the following in relation with tests:

- Linear association between dependent and independent variables is determined using both T-test and ANOVA F-tests
- Adjusted R-Squared value is used to determine the impact of introducing new independent variable in the regression model. As like R-squared value which increases with addition of new predictor variables, adjusted R-squared value increased only when there is a positive impact or else the value of adjusted R-squared value decreases.

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 4

#### The value of R-squared does not depend upon the data points; Rather it only depends upon the value of parameters

#### The value of correlation coefficient and coefficient of determination is used to study the strength of relationship in ________

#### Which of the following tests can be used to determine whether a linear association exists between the dependent and independent variables in a simple linear regression model?

#### In order to estimate population parameter, the null hypothesis is that the population parameter is ________ to zero?

#### Which of the following can be used for learning the value of parameters for regression model for population and not just the samples?

#### The value of R-Squared _________ with addition of every new independent variable?

#### In order to reject the null hypothesis while estimating population parameter, p-value has to be _______

#### The value of ____________ may increase or decrease based on whether a predictor variable enhances the model or not

#### The value of Adjusted R-squared _________ if the predictor variable enhances the model less than what is predicted by chance?

#### In regression model t-tests, the value of t-test statistics is equal to ___________?

Share your Results:

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:

- Correlation Concepts, Matrix & Heatmap using Seaborn - September 29, 2020
- Beta Distribution Explained with Python Examples - September 24, 2020
- Bernoulli Distribution Explained with PythonExamples - September 23, 2020