Regression MCQs

20 min
Score: 0
Attempted: 0/20
Subscribe

1. What is regression analysis mainly used for?
(A) Predicting continuous values
(B) Data visualization
(C) Classification of data
(D) Data cleaning

2. Which type of regression is used when there is one independent variable and one dependent variable?
(A) Simple Linear Regression
(B) Multiple Linear Regression
(C) Logistic Regression
(D) Polynomial Regression

3. In linear regression, what does the dependent variable represent?
(A) Predictor
(B) Input
(C) Constant
(D) Outcome

4. Which of the following is NOT a type of regression?
(A) K-Means Regression
(B) Logistic Regression
(C) Linear Regression
(D) Polynomial Regression

5. What is the equation of a simple linear regression line?
(A) y = mx + c
(B) y = ax² + bx + c
(C) y = log(x)
(D) y = eˣ

6. What does the slope (m) in linear regression represent?
(A) Error
(B) Intercept
(C) Mean value
(D) Rate of change

7. Which regression technique is used for binary outcomes?
(A) Linear Regression
(B) Ridge Regression
(C) Polynomial Regression
(D) Logistic Regression

8. What does R-squared measure in regression?
(A) Error rate
(B) Strength of relationship
(C) Correlation coefficient
(D) Variance explained by the model

9. Which assumption is required for linear regression?
(A) Non-linearity
(B) Multicollinearity
(C) Linearity
(D) Random guessing

10. What is multicollinearity?
(A) High error rate
(B) Missing values
(C) Low sample size
(D) High correlation between independent variables

11. Which regression reduces overfitting by adding a penalty term?
(A) Ridge Regression
(B) Logistic Regression
(C) Linear Regression
(D) Simple Regression

12. Lasso regression is mainly used for:
(A) Classification
(B) Data normalization
(C) Feature selection
(D) Clustering

13. What does the intercept (c) represent in regression?
(A) Maximum value
(B) Minimum value
(C) Mean of y
(D) Value of y when x = 0

14. Which error is minimized in linear regression?
(A) Squared error
(B) Absolute error
(C) Relative error
(D) Random error

15. Which regression model fits a curved relationship?
(A) Linear Regression
(B) Polynomial Regression
(C) Logistic Regression
(D) Ridge Regression

16. What is overfitting in regression?
(A) Model performs well on new data
(B) Model fits training data too closely
(C) Model is too simple
(D) Model ignores data

17. Which metric is commonly used to evaluate regression models?
(A) Accuracy
(B) Precision
(C) Confusion Matrix
(D) Mean Squared Error

18. What is the role of independent variables in regression?
(A) To predict the outcome
(B) To measure error
(C) To store results
(D) To remove noise

19. Which regression is suitable for predicting probabilities?
(A) Linear Regression
(B) Polynomial Regression
(C) Logistic Regression
(D) Simple Regression

20. Which condition occurs when residuals have constant variance?
(A) Homoscedasticity
(B) Normality
(C) Multicollinearity
(D) Linearity