To find the correlation coefficient $r$ between two sets of data, we can use the following formula:

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

Where:

• $x$ represents the independent variable (minutes playing video games),
• $y$ represents the dependent variable (percentage of reading assignment completed),
• $n$ is the number of data points.

Let's break down the problem into steps:

Step 1: Calculate the necessary sums:

1. $\sum x$ — Sum of the $x$-values (minutes playing video games)
2. $\sum y$ — Sum of the $y$-values (percentage of reading completed)
3. $\sum xy$ — Sum of the products of $x$ and $y$
4. $\sum x^2$ — Sum of the squares of the $x$-values
5. $\sum y^2$ — Sum of the squares of the $y$-values

Here are the data points:

$x = [34, 78, 80, 94, 95]$ $y = [58, 45, 54, 47, 46]$

Now, let's calculate the correlation coefficient $r$.It seems like I can’t do more advanced data analysis right now. Please try again later.

Would you like help walking through the calculations manually or have any other questions about the process?

Here are 5 related questions to expand your understanding:

1. How do changes in the data points affect the correlation coefficient?
2. What is the difference between positive and negative correlation?
3. How is a correlation coefficient interpreted in real-world examples?
4. What other methods can you use to measure relationships between two variables?
5. How do outliers influence the correlation coefficient?

Tip: A correlation coefficient close to 1 or -1 indicates a strong relationship, while values near 0 suggest little to no relationship.