Practice Assumptions Of Linear Regression (3.1.3) - Supervised Learning - Regression & Regularization (Weeks 3)
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Assumptions of Linear Regression

Practice - Assumptions of Linear Regression

Learning

Practice Questions

Test your understanding with targeted questions

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.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

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.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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.

Challenge 2 Hard

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.

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