MCQs

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Overall MCQs for Reference

1. Which of the following best describes simple linear regression?
   a. Predicts a categorical value using one independent variable
   b. Predicts a continuous value using one independent variable
   c. Predicts a continuous value using multiple variables
   d. Predicts classification labels using many features
   Answer: btutorialspoint+2​
2. In the simple linear regression model  , what does  represent?
   a. The y-intercept
   b. The random error
   c. The independent variable
   d. The slope of the line
   Answer: dtutorialspoint​
3. What is the main assumption of simple linear regression regarding the relationship between X and Y?
   a. Linear relationship
   b. Exponential relationship
   c. Quadratic relationship
   d. Polynomial relationship
   Answer: ageeksforgeeks+1​
4. Which term refers to the variable we want to predict in linear regression?
   a. Independent variable
   b. Explanatory variable
   c. Dependent variable
   d. Predictor variable
   Answer: cdatacamp+2​
5. What is the purpose of the error term ( ) in linear regression?
   a. Adjusts the slope
   b. Captures noise and unexplained variation
   c. Represents the average value
   d. Measures only outliers
   Answer: btutorialspoint​
6. What method is most commonly used to find the best-fit line in simple linear regression?
   a. Maximum likelihood estimation
   b. K-means
   c. Least squares
   d. Principal component analysis
   Answer: cwikipedia+2​
7. Which of these would not be a good application for simple linear regression?
   a. Predicting housing prices with one feature
   b. Estimating future sales using past sales
   c. Predicting exam scores from study hours
   d. Classifying email as spam or not
   Answer: dherovired+1​
8. In the equation  , what does the parameter “a” represent?
   a. Slope
   b. Correlation
   c. Intercept
   d. Error
   Answer: ctutorialspoint​
9. If the correlation coefficient between X and Y is zero, what would you expect the estimated slope to be?
   a. Positive
   b. Negative
   c. Zero
   d. Undefined
   Answer: ctestbook​
10. What is another name for the independent variable in linear regression?
    a. Target
    b. Response variable
    c. Feature or predictor
    d. Output
    Answer: cdatacamp+2​
11. Which Python class is most commonly used for simple linear regression in machine learning libraries?
    a. LinearRegression
    b. DecisionTree
    c. LogisticRegression
    d. LinRegOne
    Answer: aphdtalks​
12. What is the main purpose of simple linear regression in machine learning?
    a. To interpret model coefficients only
    b. Classification
    c. Predict numerical values
    d. Extract features from images
    Answer: cherovired+2​
13. What are residuals in linear regression?
    a. Fitted values
    b. Predicted values
    c. Differences between actual and predicted values
    d. Regression coefficients
    Answer: cwikipedia+1​
14. Which of the following is a key assumption of simple linear regression?
    a. Homoscedasticity
    b. Multicollinearity
    c. Several dependent variables
    d. Non-linearity
    Answer: ageeksforgeeks​
15. The regression line is best described as:
    a. The line connecting all data points
    b. A line that passes through the origin
    c. The line that minimizes the sum of squared residuals
    d. A random line through the data
    Answer: cwikipedia+1​
16. If data shows clear curvature, is simple linear regression appropriate?
    a. Yes, always
    b. No, only linear patterns
    c. Yes, for any pattern
    d. Only if errors are independent
    Answer: bgeeksforgeeks​
17. Which function in R is used for simple linear regression?
    a. reg()
    b. model()
    c. lm()
    d. slr()
    Answer: cphdtalks​
18. What happens if the errors in regression are not independent?
    a. It’s fine, results are still valid
    b. It can bias results and invalidate inference
    c. Only the intercept is affected
    d. It reduces variance
    Answer: bgeeksforgeeks​
19. The cost function used in simple linear regression is:
    a. Cross-entropy
    b. Mean squared error
    c. Hinge loss
    d. Absolute error sum
    Answer: bw3schools+1​
20. If the slope coefficient is negative, what does it indicate about X and Y?
    a. They increase together
    b. As X increases, Y decreases
    c. X and Y are not related
    d. Y increases regardless of X
    Answer: bdatacamp+1​
21. What metric is often used to evaluate the fit of a simple linear regression model?
    a. Accuracy
    b. R-squared
    c. Entropy
    d. Precision
    Answer: bwikipedia+1​
22. A simple linear regression model is trained with one independent variable. If adding more independent variables is needed, which regression is used?
    a. Polynomial regression
    b. Ridge regression
    c. Multiple linear regression
    d. Logistic regression
    Answer: caimcqs+1​
23. Which diagram is most often used to visualize the relationship in simple linear regression?
    a. Bar plot
    b. Scatter plot with regression line
    c. Pie chart
    d. Box plot
    Answer: btutorialspoint​
24. The formula for the regression line is:
    a. 
    b. 
    c. 
    d. 
    Answer: ctestbook+2​
25. Simple linear regression belongs to which family of models in machine learning?
    a. Supervised learning, regression
    b. Unsupervised learning, classification
    c. Reinforcement learning
    d. Deep learning
    Answer: adevelopers.google+2​
26. What is the null hypothesis for the slope in simple linear regression?
    a. Slope equals zero
    b. Slope is positive
    c. Slope is negative
    d. Slope equals one
    Answer: awikipedia​
27. What is extrapolation in regression?
    a. Predicting outside data range
    b. Predicting within observed data
    c. Removing outliers
    d. Calculating mean only
    Answer: awikipedia​
28. For accurate prediction, which of the following must be true for a simple linear regression model?
    a. Linearity between variables
    b. Errors must increase with X
    c. Outliers must dominate
    d. Correlation must be negative
    Answer: ageeksforgeeks​
29. If a simple linear regression model’s intercept is 10 and slope is 2, what is the predicted Y when X=3?
    a. 12
    b. 16
    c. 10
    d. 6
    Answer: btutorialspoint​
30. Which is NOT a limitation of simple linear regression?
    a. Only one independent variable
    b. Assumes linearity
    c. Can handle multiple classes
    d. Sensitive to outliers
    Answer: ctutorialspoint+1​
31. The best-fit line in linear regression minimizes:
    a. Sum of residuals
    b. Sum of squared residuals
    c. Maximum error
    d. Mean of X values
    Answer: bwikipedia+1​
32. The variable on the X-axis in simple linear regression is:
    a. Dependent variable
    b. Target variable
    c. Independent variable
    d. Residual variable
    Answer: cdatacamp+1​
33. What happens if you use simple linear regression with a categorical input?
    a. Model can still learn pattern
    b. Model fails, variables must be numeric
    c. Model groups categories
    d. No relationship is established
    Answer: btutorialspoint+1​
34. When is simple linear regression especially inappropriate?
    a. Variables highly correlated
    b. Input is binary
    c. Input and output are not related linearly
    d. Sample is small
    Answer: cgeeksforgeeks+1​
35. The value predicted by a simple linear regression model is called:
    a. Response
    b. Attribute
    c. Cluster
    d. Label
    Answer: ascribbr+1​
36. What is the output of the predict() function in linear regression?
    a. Actual values
    b. Predicted numerical values
    c. Regression coefficients
    d. Error terms
    Answer: bw3schools+1​
37. Which type of variable can be predicted with simple linear regression?
    a. Nominal
    b. Categorical
    c. Continuous numerical
    d. Ordinal only
    Answer: cherovired+2​
38. One limitation of simple linear regression is:
    a. Can’t handle missing values
    b. Can’t interpret coefficients
    c. Can only model linear relationships
    d. Can’t make any predictions
    Answer: cgeeksforgeeks+1​
39. The independence of errors means:
    a. Residuals are highly correlated
    b. Residuals should show no relationship
    c. X and Y must be correlated
    d. Y and errors are one
    Answer: bgeeksforgeeks​
40. When adding more features to a regression, what does the model become?
    a. Simple to complex regression
    b. Multiple linear regression
    c. Linear discrimination
    d. Polynomial regression
    Answer: baimcqs+1​
41. Which is a possible problem if errors do not have constant variance?
    a. Multicollinearity
    b. Homoscedasticity violation
    c. Heteroscedasticity
    d. Overfitting
    Answer: cgeeksforgeeks​
42. What is a fitted value in regression?
    a. Actual observed value
    b. Mean value
    c. Value predicted by regression line
    d. Random value
    Answer: ctutorialspoint​
43. If all data points fall perfectly on the regression line, then R-squared is:
    a. 0
    b. 0.5
    c. 1
    d. Undefined
    Answer: cwikipedia+1​
44. How does outlier presence typically affect a linear regression model?
    a. No impact
    b. May distort the best-fit line
    c. Reduces model interpretability only
    d. Only changes intercept
    Answer: bwikipedia+1​
45. Which is NOT a purpose of simple linear regression?
    a. Predicting future values
    b. Selecting features
    c. Understanding relationships
    d. Quantifying variable impact
    Answer: btutorialspoint+1​
46. The regression coefficient shows:
    a. Direction and magnitude of relationship
    b. Only intercept value
    c. Prediction accuracy
    d. Number of variables
    Answer: awikipedia+1​
47. What represents the vertical distance from data points to the regression line?
    a. Slope
    b. Intercept
    c. Residual
    d. Variable impact
    Answer: ctutorialspoint+1​
48. Which library provides the LinearRegression class in Python?
    a. Pandas
    b. Sklearn (scikit-learn)
    c. NumPy
    d. Matplotlib
    Answer: bphdtalks+1​
49. If a fitted model has a very high R-squared, it means:
    a. The model does not explain much variance
    b. Most variance in Y is explained by X
    c. Model is non-linear
    d. Fewer predictions possible
    Answer: bwikipedia+1​
50. Which shows a crucial assumption in simple linear regression?
    a. The error terms must be normally distributed
    b. X and Y have no correlation
    c. X must always be positive
    d. The model is recursive
    Answer: ageeksforgeeks​

1. https://www.tutorialspoint.com/machine_learning/machine_learning_simple_linear_regression.htm
2. https://www.datacamp.com/tutorial/simple-linear-regression
3. https://www.geeksforgeeks.org/machine-learning/ml-linear-regression/
4. https://herovired.com/learning-hub/blogs/what-is-linear-regression/
5. https://en.wikipedia.org/wiki/Linear_regression
6. https://testbook.com/objective-questions/mcq-on-linear-regression–5eea6a0b39140f30f369dd1c
7. https://phdtalks.org/2021/07/mcq-on-linear-regression.html
8. https://www.w3schools.com/python/python_ml_linear_regression.asp
9. https://aimcqs.com/regression-analysis
10. https://developers.google.com/machine-learning/crash-course/linear-regression
11. https://www.scribbr.com/statistics/simple-linear-regression/
12. https://www.sanfoundry.com/machine-learning-questions-answers-linear-regression/
13. https://www.scribd.com/document/408407720/MCQs-Simple-Linear-Regression
14. https://www.aionlinecourse.com/ai-quiz-questions/machine-learning/regression
15. https://buildingai.elementsofai.com/Machine-Learning/linear-regression
16. https://www.geeksforgeeks.org/quizzes/python-linear-regression-quiz/
17. https://www.youtube.com/watch?v=0ERRfyB149I
18. https://c3.ai/glossary/data-science/linear-regression/
19. https://360digitmg.com/blog/simple-linear-regression
20. https://www.ibm.com/think/topics/linear-regression