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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