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

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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:

- Agentic Reasoning Design Patterns in AI: Examples - October 18, 2024
- LLMs for Adaptive Learning & Personalized Education - October 8, 2024
- Sparse Mixture of Experts (MoE) Models: Examples - October 6, 2024

I found it very helpful. However the differences are not too understandable for me