Practice Implement Simple Linear Regression - 4.1.2 | Module 2: Supervised Learning - Regression & Regularization (Weeks 3) | Machine Learning
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4.1.2 - Implement Simple Linear Regression

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the dependent variable in a simple linear regression model?

πŸ’‘ Hint: Think about what outcome we want to understand.

Question 2

Easy

What does the slope (Ξ²1) represent in simple linear regression?

πŸ’‘ Hint: It tells us how responsive one variable is to another.

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 does the dependent variable (Y) represent in simple linear regression?

  • It is the variable we predict
  • It is the variable we control
  • It is the variable we manipulate

πŸ’‘ Hint: Think about what you want to understand or determine.

Question 2

True or False: In simple linear regression, the relationship must be linear.

  • True
  • False

πŸ’‘ Hint: Consider how the variables interact with one another.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You are given a dataset of house prices based on their size in square feet. Describe how you would implement simple linear regression to predict house prices. Discuss any assumptions you would need to check.

πŸ’‘ Hint: Consider how size relates to priceβ€”does it seem linear? What checks can assure your model's reliability?

Question 2

Given a regression model that predicts sales based on advertising spend, if your model shows a low RΒ² value, what does that imply, and what next steps could you take?

πŸ’‘ Hint: Reflect on what additional factors could affect sales beyond just advertising.

Challenge and get performance evaluation