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The image shows a data set where a linear regression was performed. The equation for the line of best fit is provided as \( y = 0.25x + 2.21 \), and the correlation coefficient \( r = 0.29 \). The task is to interpret the correlation coefficient, which was identified as a "weak positive correlation."

Here's the breakdown:
- **Line of Best Fit**: \( y = 0.25x + 2.21 \). The slope \( 0.25 \) shows a positive trend, meaning as \( x \) increases, \( y \) also increases, but not very steeply.
- **Correlation Coefficient \( r = 0.29 \)**: A value of 0.29 indicates a weak positive linear relationship. The correlation is positive since the value is above 0, but weak because it is closer to 0 than to 1.

This means that there is a slight positive association between the variables \( x \) and \( y \), but the relationship isn't very strong.

Would you like to dive into any details, or have specific questions?

Here are some follow-up questions:
1. What does the slope \( 0.25 \) represent in the context of the data?
2. How would a stronger positive correlation (e.g., \( r = 0.9 \)) affect the interpretation?
3. What would the equation look like if the correlation were negative?
4. How does the intercept \( 2.21 \) affect the line of best fit?
5. Could any outliers in the data significantly affect the value of \( r \)?

**Tip**: The closer the correlation coefficient \( r \) is to 1 or -1, the stronger the linear relationship between the two variables.

Use the linear regression feature on a graphing calculator to find an equation of the line of best fit and correlation coefficient for the data. Round all values to the nearest hundredth.

Learn how to use the linear regression feature on a graphing calculator to find the equation of the line of best fit (y = 0.25x + 2.21) and interpret a weak positive correlation (r = 0.29). Suitable for high school students in Grades 9-12.

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