To find the correlation coefficient ($r$) and the equation of the regression line, we can use the following formulas:

1. Correlation Coefficient ($r$):

The correlation coefficient $r$ is given by:

$r = \frac{n \cdot \Sigma xy - \Sigma x \cdot \Sigma y}{\sqrt{\left( n \cdot \Sigma x^2 - (\Sigma x)^2 \right) \cdot \left( n \cdot \Sigma y^2 - (\Sigma y)^2 \right)}}$

However, since $\Sigma x^2$ is not provided, we can't directly calculate $r$ from the given information. It seems there's some missing information. Usually, $\Sigma x^2$ is also provided along with the other values.

2. Regression Equation ($y = mx + c$):

The regression line $y$ on $x$ can be represented as:

$y = mx + c$

Where:

• $m$ is the slope given by: $m = \frac{n \cdot \Sigma xy - \Sigma x \cdot \Sigma y}{n \cdot \Sigma x^2 - (\Sigma x)^2}$

• $c$ is the y-intercept given by: $c = \frac{\Sigma y - m \cdot \Sigma x}{n}$

Again, since $\Sigma x^2$ is missing, we can’t find the exact values of $r$, $m$, and $c$.

Conclusion:

Without $\Sigma x^2$, the correlation coefficient and the regression equation cannot be fully determined.

Would you like to provide more information or do you have any questions?

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Here are 5 related questions:

1. How can the correlation coefficient help in understanding the relationship between two variables?
2. What is the significance of the slope in a regression equation?
3. How does the y-intercept influence the position of the regression line?
4. What are some common applications of regression analysis in real-life situations?
5. How can you interpret the value of the correlation coefficient?

Tip: When solving statistical problems, always ensure you have all the necessary data points for accurate calculations.