Practice - Linear Regression
Practice Questions
Test your understanding with targeted questions
What does the equation Y = β0 + β1X + ϵ represent?
💡 Hint: Think about how predictions are made.
What does R-squared measure?
💡 Hint: Consider the importance of the independent variables' impact.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What does MSE stand for?
💡 Hint: Think about how errors are squared in this metric.
True or False: The slope in linear regression indicates the expected change in the dependent variable for a one-unit increase in the independent variable.
💡 Hint: Remember the significance of the slope in the regression equation.
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Challenge Problems
Push your limits with advanced challenges
You are trying to predict housing prices based on size, location, and number of bathrooms using multiple linear regression. However, you see that the coefficients for size and location are very high. What steps can you take to address potential multicollinearity?
💡 Hint: Think about the relationships between your predictors.
Consider a dataset where you apply polynomial regression. After fitting, you notice the model performs flawlessly on training data but poorly on testing data. What is happening here, and what steps could you take?
💡 Hint: Visualize the trade-off between model complexity and data fit.
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Reference links
Supplementary resources to enhance your learning experience.