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)
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:
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