Simple Linear Regression Quiz by Shubhrata Shrestha | Oct 8, 2025 | 0 comments Simple Linear Regression Quiz 1. What does simple linear regression primarily analyze? A. Relationship between two variables B. Relationship among multiple variables C. The mean of a dataset D. The mode of a dataset None 2. In a scatter diagram, a strong positive correlation means: A. One variable decreases when the other increases B. Both variables move closely in the same direction C. There is no relationship between the variables D. The variables are unrelated None 3. Which equation represents the form of simple linear regression? A. Y = ab + X B. X = a + bY C. Y = a + bX D. X = a + b None 4. What does the line of best fit show? A. Random fluctuations in data B. The trend or relationship between two variables C. The difference between means D. The correlation coefficient None 5. Extrapolation is best defined as: A. Measuring the relationship between unrelated data B. Estimating data values beyond the existing dataset C. Removing outliers from a dataset D. Calculating mean deviation None 6. Which of the following is an example of a strong negative correlation? A. Increase in advertising and increase in sales B. Increase in smartphone sales and increase in phone cases C. Increase in Coca-Cola price and decrease in Pepsi demand D. Increase in dessert sales and increase in meal sales None 7. What is the major limitation of using simple linear regression in business forecasting? A. It always gives exact results B. It ignores qualitative factors and external variables C. It works better with complex datasets D. It eliminates outliers automatically None 8. Correlation and causation differ because: A. Correlation always proves cause and effect B. Causation is weaker than correlation C. Correlation shows association but not direct cause D. Causation is based on unrelated variables None 9. Which factor can distort regression results the most? A. Accurate data B. Random variations and outliers C. Consistent patterns D. Steady correlation None 10. One key benefit of using simple linear regression is: A. It ensures perfect predictions B. It provides quantitative justification for business decisions C. It removes seasonal variations D. It prevents outliers in data None Time's up Submit a Comment Cancel replyYour email address will not be published. Required fields are marked *Comment * Name * Email * Website Save my name, email, and website in this browser for the next time I comment. Δ
