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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
Define the linearity assumption in linear regression.
💡 Hint: Think about how a line fits the data points.
Question 2
Easy
What does the term multicollinearity refer to?
💡 Hint: Consider how this affects variable assessment in models.
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 assumption related to the relationship between independent and dependent variables in linear regression?
- A. Linearity
- B. Independence of Errors
- C. Homoscedasticity
💡 Hint: Recall the importance of a straight line in this context.
Question 2
True or False: Normality of errors is vital solely for predictive accuracy but not for inference.
- True
- False
💡 Hint: Think about statistical tests.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have a dataset showing advertising spend and sales revenue but notice a non-linear trend. How would you proceed with creating a predictive model?
💡 Hint: Look for curves in your scatter plot.
Question 2
Your regression model shows multicollinearity with a VIF of 15 for one of the independent variables. Discuss potential remedies.
💡 Hint: Consider how simplifying your model could enhance clarity.
Challenge and get performance evaluation