Practice Simple and Multiple Linear Regression - 3.1 | Module 2: Supervised Learning - Regression & Regularization (Weeks 3) | Machine Learning
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3.1 - Simple and Multiple Linear Regression

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define what a dependent variable is.

πŸ’‘ Hint: It's what you're trying to predict.

Question 2

Easy

What is the role of an independent variable in regression?

πŸ’‘ Hint: Think of it as an ingredient in a recipe.

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 slope (Ξ²1) represent in a simple linear regression?

  • The Y-intercept
  • The change in Y for a change in X
  • The error in the prediction

πŸ’‘ Hint: Think about how the independent variable impacts the dependent variable.

Question 2

True or False: In linear regression, it is acceptable to have multicollinearity among independent variables.

  • True
  • False

πŸ’‘ Hint: Consider how related predictors might complicate interpretation.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Create a multiple linear regression model using hours studied, previous test scores, and attendance rates as predictors for predicting final exam scores. Analyze the output coefficients.

πŸ’‘ Hint: Look for which predictor has the highest coefficient.

Question 2

Explain how a violation of the homoscedasticity assumption can affect your model's predictions.

πŸ’‘ Hint: Consider how varied error might mislead predictions.

Challenge and get performance evaluation