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Multiple Linear Regression Interview Questions & Answers
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Table of Content
- This model considers the fewest number of predictor/explanatory variables, in comparison with other models, which are nearly equivalent in terms of significance and fit
- 2. In MLR, if there are three variables, what would be the shape?
- If there are more than three variables, it is not possible to visualize them.
- In MLR the shape is not really a line.
- To visualize all the pair of variables at a time, we use
- Reducing the number of features and computational complexity of the model is done by
- In regression ________ are the feature selection methods used .
- We use the ______ plot to know whether there is a relationship between two variables.
- After fitting the model with all the required variables______ method is used for identifying and removing independent variables that do not contribute enough to the model.
- Considering variables one by one and building the model by checking the significance value & R square is done by using _____ method.
- Using the regression model, we can estimate the strength and direction of the association from the adjusted partial regression of Independent Variables
- ____________increases only when independent variable is significant and affects dependent variable.
- R square=
- A lower AIC or BIC value indicates a _______ fit.
- Will you be able to improve your linear regression model by making it more complex i.e. by adding more linear regression variables to it?
-
This model considers the fewest number of predictor/explanatory variables, in comparison with other models, which are nearly equivalent in terms of significance and fit
- a) Law of MLR
- b) Law of equations
- c) Law of Parsimony
- d) Law of Parsinomy
Answer - c) Law of Parsimony
-
In MLR, if there are three variables, what would be the shape?
- a) Circle
- b) Hyperplane
- c) Triangle
- d) None of the above
Answer - b) Hyperplane
-
If there are more than three variables, it is not possible to visualize them.
- a) True
- b) False
Answer - a) True
-
In MLR the shape is not really a line.
- a) True
- b) False
Answer - a) True
-
To visualize all the pair of variables at a time, we use
- a) Scatter plot
- b) Box plot
- c) Scatter plot matrix
- d) None of the above
Answer - c) Scatter plot matrix
-
Reducing the number of features and computational complexity of the model is done by
- a) Feature selection
- b) Cross selection
- c) Complete selection
- d) None of the above
Answer - a) Feature selection
-
In regression ________ are the feature selection methods used .
- a) Forward selection
- b) Backward elimination
- c) Stepwise regression
- d) All the above
Answer - d) All the above
-
We use the ______ plot to know whether there is a relationship between two variables.
- a) Histogram
- b) Box plot
- c) Scatter plot
- d) Bar plot
Answer - c) Scatter plot
-
After fitting the model with all the required variables______ method is used for identifying and removing independent variables that do not contribute enough to the model.
- a) Forward selection
- b) Backward elimination
- c) Stepwise regression
- d) All the above
Answer - b) Backward elimination
-
Considering variables one by one and building the model by checking the significance value & R square is done by using _____ method.
- a) Stepwise Regression
- b) Backward elimination
- c) Both b and c
- d) Stepwise elimination
Answer - a) Stepwise Regression
-
Using the regression model, we can estimate the strength and direction of the association from the adjusted partial regression of Independent Variables
- a) True
- b) False
Answer - a) True
-
____________increases only when independent variable is significant and affects dependent variable.
- a) R-squared
- b) Adjusted R-squared
- c) Both the above
- d) None of the above
Answer - b) Adjusted R-squared
-
R square=
- a) SSR/SSE
- b) SSE/SSR
- c) SSS/SEE
- d) SSR/SST
Answer - d) (Sum Squared Regression (SSR)/Total Sum of Squares (SST)
-
A lower AIC or BIC value indicates a _______ fit.
- a) Worst fit
- b) Better fit
- c) Low fit
- d) None of the above
Answer - b) Better fit
-
Will you be able to improve your linear regression model by making it more complex i.e. by adding more linear regression variables to it?
- a) Yes
- b) No
Answer - b) No
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