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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does the linearity assumption imply in regression analysis?
💡 Hint: Think about the shape of the graph.
Question 2
Easy
Why is homoscedasticity important in regression?
💡 Hint: Think about what happens if variances change.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the main requirement of the linearity assumption?
- The output should be discrete
- The relationship should be linear
- The independent variables should be correlated
💡 Hint: Think about how we visualize relationships.
Question 2
True or False: Homoscedasticity means that the variance of errors can vary at different levels of the independent variable.
- True
- False
💡 Hint: What does 'homo' suggest about the variances?
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You suspect that your model violates the homoscedasticity assumption. Describe a detailed approach to investigate and remedy this issue.
💡 Hint: Think about visual diagnostics and their implications.
Question 2
Your regression model has three predictors, and you detect multicollinearity. Explain how you would analyze and address the issue.
💡 Hint: Consider the impact of correlated variables on model interpretation.
Challenge and get performance evaluation