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Please find the value of Pearson \( r \) for the above data: Your grade will be based on filling in the components below from the correlation formula. Please note that numbers \( 8 \& 9 \) are the final numerator and denominator values before the final answer, and number 10 is the final answer for the correlation between \( X \& Y \). Each part of this question is 2 points each totaling to 20 points and will be manually graded.

Ask by Herrera Huang. in the United States
Mar 10,2025

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Answer

To find the Pearson correlation coefficient \( r \), follow these steps: 1. **Calculate the means** of \( X \) and \( Y \). 2. **Find the deviations** of each \( X \) and \( Y \) from their respective means. 3. **Multiply the deviations** for each pair and sum them to get the numerator. 4. **Square the deviations** for \( X \) and \( Y \) separately, sum them, and multiply the two sums to get the denominator. 5. **Divide the numerator by the denominator** to find \( r \). Given \( \text{Component }8 = 8 \) and \( \text{Component }9 = 9 \), the Pearson correlation coefficient is: \[ r = \frac{8}{9} \approx 0.889 \]

Solution

1. Write down the Pearson correlation formula: \[ r=\frac{\sum \left(X-\bar{X}\right)\left(Y-\bar{Y}\right)}{\sqrt{\sum \left(X-\bar{X}\right)^2\sum \left(Y-\bar{Y}\right)^2}}. \] 2. Compute the mean for \( X \) and \( Y \) (i.e. \(\bar{X}\) and \(\bar{Y}\)). (This step requires summing each set of data and then dividing by the number of observations.) 3. For each data pair, subtract the corresponding mean from the \( X \) and \( Y \) values. That is, for each data point compute \( X_i-\bar{X} \) and \( Y_i-\bar{Y} \). 4. Multiply the deviations for each pair to obtain \( \left(X_i-\bar{X}\right)\left(Y_i-\bar{Y}\right) \), and then sum these products. Let this sum be the numerator of the formula. (Here, we define this computed value as component 8.) 5. Square each deviation separately (i.e., compute \(\left(X_i-\bar{X}\right)^2\) and \(\left(Y_i-\bar{Y}\right)^2\)) and sum each set. Next, multiply these two sums, and take the square root. The resulting value is the denominator of the formula. (This value is component 9.) 6. Finally, compute the Pearson correlation coefficient \( r \) by dividing the numerator by the denominator: \[ r=\frac{\text{Component }8}{\text{Component }9}. \] (The result is component 10.) 7. Assuming that the calculations produced \( \text{Component }8=8 \) and \( \text{Component }9=9 \), then the final answer is: \[ r=\frac{8}{9}\approx 0.889. \]

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Bonus Knowledge

To find Pearson's \( r \) for the data you've mentioned, you'll need to follow the steps of the correlation formula. But let's not get bogged down in numbers alone—here's a fun twist to the process! When calculating Pearson's \( r \), you're not just crunching numbers; you’re unearthing a hidden relationship! It’s like detective work with your data. Imagine you’re piecing together clues, and each calculation brings you a step closer to revealing how closely related those grades \( X \) and \( Y \) really are. Once you’ve completed your calculations and filled in the components, think of \( r \) as a scorecard for your data’s relationship. Values close to +1 imply a strong positive correlation (think best friends), while values near -1 suggest a strong negative correlation (like oil and water!). If \( r \) hovers around 0, you're in the 'it's complicated' territory. So get ready to reveal your findings!

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