This page lists down 40 regression (linear / univariate, multiple / multilinear / multivariate) interview questions (in form of objective questions) which may prove helpful for Data Scientists / Machine Learning enthusiasts. Those appearing for interviews for machine learning / data scientist freshers / intern / beginners position would also find these questions very helpful and handy enough to quickly brush up / check your knowledge and prepare accordingly.
Table of Contents
Practice Tests on Regression Analysis
These interview questions are split into four different practice tests with questions and answers which can be found on following page:
- Linear, Multiple regression interview questions and answers – Set 1
- Linear, Multiple regression interview questions and answers – Set 2
- Linear, Multiple regression interview questions and answers – Set 3
- Linear, Multiple regression interview questions and answers – Set 4
Regression Topics covered in these Practice Tests
Some of the following topics have been covered in these questions:
- Introduction to linear (univariate) and multi-linear / multiple (multivariate) regression
- Concepts related with coefficient of determination vis-a-vis pearson correlation coefficient
- Evaluation of regression models using different techniques such as t-tests, analysis of variance f-tests
- Sum of squares calculations and related concepts
- Concepts related with R-squared, adjusted R-squared
Linear / Multi-linear Regression Questions and Answers
- In ________ regression, there is _______ dependent variable and ________ independent variable(s)
- Simple linear, one, multiple
- Multiple, multiple, one
- Simple linear, one, one
- Multiple, one, multiple
- In ________ regression, there is _______ dependent variable and ________ independent variable(s)
- Simple linear, multiple, one
- Simple linear, one, multiple
- Multiple, one, multiple
- Multiple, multiple, multiple
- 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
- True
- False
- Linear or multilinear regression helps in predicting _______
- Continuous valued output
- Discrete valued output
- Regression analysis helps in studying __________ relationship between variables.
- Deterministic
- Statistical
- Regression analysis helps in doing which of the following?
- Causal analysis
- Effects in forecasting
- Forecasting trends
- All of the above
- The best fit line is achieved by finding values of the parameters which minimizes the sum of __________
- Prediction errors
- Squared prediction errors
- Best fit line is also termed as _______
- Maximum squares regression line
- Least squares regression line
- Which of the following can be used to understand the statistical relationship between dependent and independent variables in linear regression?
- Coefficient of determination
- Correlation coefficient
- Both of the above
- None of the above
- It is absolutely OK to state that correlation does imply causation
- True
- False
- The value of coefficient of determination, R-squared, is _________
- Less than 0
- Greater than 1
- Between 0 and 1
- Which of the following can be used to understand the positive or negative relationship between dependent and independent variables
- Coefficient of determination
- Pearson correlation coefficient
- The goal of the regression model is to achieve the R-squared value ________
- Closer to 0
- Closer to 1
- More than 1
- Less than 1
- Pearson correlation coefficient is __________ to coefficient of determination
- Directly proportional
- Inversely proportional
- Pearson correlation coefficient does always have positive value
- True
- False
- Value of Pearson correlation coefficient near to zero represents the fact there is a stronger relationship between dependent and independent variables
- True
- False
- Population correlation coefficient and sample correlation coefficient are one and the same
- True
- False
- The value of Pearson correlation coefficient falls in the range of _________
- 0 and 1
- 0 and -1
- -1 and 1
- 1 and 2
- The value of correlation coefficient and R-squared remains same for all samples of data
- True
- False
- The large value of R-squared can be safely interpreted as the fact that estimated regression line fits the data well.
- True
- False
- The value of R-squared does not depend upon the data points; Rather it only depends upon the value of parameters
- True
- False
- The value of correlation coefficient and coefficient of determination is used to study the strength of relationship in ________
- Samples only
- Both Samples and Population
- Population only
- 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?
- T-test
- ANOVA F-test
- Both of the abov
- None of the abovee
- In order to estimate population parameter, the null hypothesis is that the population parameter is ________ to zero?
- Equal
- Not equal
- Which of the following can be used for learning the value of parameters for regression model for population and not just the samples?
- Hypothesis testing
- Confidence intervals
- Both of the above
- None of the above
- The value of R-Squared _________ with addition of every new independent variable?
- May increase or decrease
- Always increases
- Always decreases
- In order to reject the null hypothesis while estimating population parameter, p-value has to be _______
- More than 0.05
- Less than 0.05
- The value of ____________ may increase or decrease based on whether a predictor variable enhances the model or not
- R-squared
- Adjusted R-squared
- The value of Adjusted R-squared _________ if the predictor variable enhances the model less than what is predicted by chance?
- Increases
- Decreases
- In regression model t-tests, the value of t-test statistics is equal to ___________?
- Coefficient divided by Standard error of coefficient
- Standard error of coefficient divided by coefficient
- Coefficient plus standard error of coefficient
- In ANOVA test for regression, degrees of freedom (regression) is _________
- Equal to number of parameters being estimated
- One more than the number of parameters being estimated
- One less than the number of parameters being estimated
- In ANOVA test for regression, degrees of freedom (regression) is _________
- Equal to number of predictor variables
- One more than the number of predictor variables
- One less than the number of predictor variables
- For SST as sum of squares total, SSE as sum of squared errors and SSR as sum of squares regression, which of the following is correct?
- SST = SSR – SSE
- SST = SSR + SSE
- SST = SSR/SSE
- The value of coefficient of determination is which of the following?
- SSR / SST
- SSE / SST
- Mean squared error can be calculated as _______
- Sum of squares error / degrees of freedom
- Sum of squares regression/ degrees of freedom
- Sum of squares total/ degrees of freedom
- Sum of Squares Regression (SSR) is ________
- Sum of Squares of predicted value minus average value of dependent variable
- Sum of Squares of Actual value minus predicted value
- Sum of Squares of Actual value minus average value of dependent variable
- Sum of Squares Error (SSE) is ________
- Sum of Squares of predicted value minus average value of dependent variable
- Sum of Squares of Actual value minus predicted value
- Sum of Squares of Actual value minus average value of dependent variable
- Sum of Squares Total (SST) is ________
- Sum of Squares of predicted value minus average value of dependent variable
- Sum of Squares of Actual value minus predicted value
- Sum of Squares of Actual value minus average value of dependent variable
- ______ the value of sum of squares regression (SSR), better the regression model
- Greater
- Lesser
- The objective for regression model is to minimize ______ and maximize ______
- SSR, SSE
- SSE, SSR
- SSR, SST
- SSE, SST
Hope you would find above set of questions along with practice tests related with linear / multiple rergression useful for next / upcoming interviews in relation with data scientist / machine learning engineer position.
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