Your answer is mostly correct and provides a good explanation. However, it can be slightly improved for clarity and conciseness. Here's a refined version:

Linear regression is a statistical method that models the relationship between two variables by fitting a straight line (line of best fit) through a set of data points. This method is used to predict the value of one variable based on the other. For example, predicting house prices (dependent variable) based on square footage (independent variable).

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Would you like more details or clarifications?
Here are five questions to expand your understanding:

1. What are the key assumptions behind linear regression?
2. How is the "line of best fit" determined in linear regression?
3. What is the role of the independent and dependent variables in linear regression?
4. How would you interpret the slope and intercept in a linear regression equation?
5. What are some limitations of using linear regression for making predictions?

Tip: Always ensure that your example is clearly tied to the concept you're explaining for better clarity.