Linear Regression - Part 2 Quiz

Linear Regression Quiz

Next Quiz-Logistic Regression MCQs

1. What is the primary goal of Linear Regression in machine learning?

   Minimize the number of features
   Maximize the accuracy of the model
   Minimize the mean squared error
   Maximize the regularization term
   

2. Which of the following assumptions is NOT required for simple linear regression?

   Linearity
   Homoscedasticity
   Independence of errors
   Multicollinearity
   

3. What does the slope coefficient in simple linear regression represent?

   The predicted value of the dependent variable
   The correlation between independent variables
   The change in the dependent variable for a one-unit change in the independent variable
   The standard error of the regression
   

4. What is multicollinearity in multiple linear regression?

   The presence of outliers in the data
   The assumption of normality in the error terms
   High correlation between independent variables
   The violation of linearity assumption
   

5. What does the p-value associated with an independent variable in linear regression indicate?

   The importance of the variable
   The correlation between the variable and the dependent variable
   The probability that the variable has no effect on the dependent variable
   The number of observations in the dataset
   

6. What is the coefficient of determination (R-squared) in linear regression?

   It measures the correlation between independent variables
   It quantifies the goodness of fit of the model
   It indicates the magnitude of the coefficients
   It helps with feature selection
   

7. When should you use Ridge Regression instead of ordinary Linear Regression?

   When you have a large number of features, some of which are highly correlated
   When you have a small dataset with a single feature
   When you want to completely eliminate some features from the model
   When you want to achieve the best possible prediction performance
   

8. What is the purpose of the residuals plot in linear regression?

   To check the linearity assumption
   To check for multicollinearity
   To assess the homoscedasticity assumption
   To identify influential outliers
   

9. What is the purpose of the feature scaling in linear regression?

   To reduce the dimensionality of the dataset
   To make the coefficients interpretable
   To speed up the model training
   To ensure variables have similar scales for accurate coefficient comparison
   

10. Which of the following is NOT an assumption of linear regression?

    Linearity
    Homoscedasticity
    Normality of independent variables
    Independence of errors
    

11. What is the purpose of the cost function in linear regression?

    To calculate the coefficient of determination (R-squared)
    To assess the normality of residuals
    To measure the model's performance
    To identify influential outliers
    

12. What is the impact of adding more features to a linear regression model?

    It always improves the model's performance
    It increases the risk of overfitting
    It reduces the need for feature selection
    It has no effect on the model's performance
    

13. What is the difference between simple linear regression and multiple linear regression?

    Simple linear regression has one independent variable, while multiple linear regression has multiple independent variables
    Simple linear regression uses a quadratic equation, while multiple linear regression uses a linear equation
    There is no difference; they are the same model
    Simple linear regression uses categorical variables, while multiple linear regression uses continuous variables
    

14. What is the purpose of residual analysis in linear regression?

    To calculate the coefficient of determination (R-squared)
    To check for multicollinearity
    To assess the homoscedasticity assumption
    To identify influential outliers
    

15. What is the impact of multicollinearity on a multiple linear regression model?

    It increases the accuracy of the model
    It makes the model more robust to outliers
    It can lead to unstable and unreliable coefficient estimates
    It simplifies the interpretation of coefficients