Determine the linear correlation coefficient between Cost and Graduation Rate.
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enter your response here ​(Round to three decimal places as​ needed.)

To calculate the linear correlation coefficient \( r \) between Cost and Graduation Rate, you will need the paired data for both variables. The linear correlation coefficient is a measure of the strength and direction of the linear relationship between two variables. The formula for \( r \) is:

\[
r = \frac{n \sum xy - \sum x \sum y}{\sqrt{\left( n \sum x^2 - (\sum x)^2 \right) \left( n \sum y^2 - (\sum y)^2 \right)}}
\]

Where:
- \( n \) is the number of data points,
- \( x \) represents the values of one variable (e.g., Cost),
- \( y \) represents the values of the other variable (e.g., Graduation Rate),
- \( \sum xy \) is the sum of the product of paired data points,
- \( \sum x^2 \) is the sum of squared values of the \( x \) variable,
- \( \sum y^2 \) is the sum of squared values of the \( y \) variable.

If you have a specific dataset, please provide it, and I can calculate the correlation coefficient for you.

Let me know if you'd like to proceed with any data or more details on the calculation.

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Here are some follow-up questions:

1. Do you have the data points for Cost and Graduation Rate?
2. Are there any outliers in the data that might affect the correlation?
3. How do you interpret the value of the correlation coefficient?
4. Would you like help on how to compute the correlation in a specific tool (like Excel)?
5. Do you need to perform any other statistical analysis on the data?

**Tip**: A correlation coefficient close to \( r = 1 \) or \( r = -1 \) indicates a strong relationship, while a value near \( r = 0 \) suggests a weak or no linear relationship.

Determine the linear correlation coefficient between Cost and Graduation Rate.

Learn how to calculate the linear correlation coefficient (r) between Cost and Graduation Rate with a detailed formula explanation. This statistical method measures the strength of the linear relationship between two variables. Suitable for Grades 11-12 or college students analyzing paired data.

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