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:
The value of coefficient of determination, R-squared, is _________
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.
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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