The data below are the temperatures on randomly chosen days during a summer class and the number of absences on those days. Calculate the correlation​ coefficient, r.
Start 2 By 10 Table 1st Row 1st Column Temperatur comma x 2nd Column 76 3rd Column 89 4st Column 95 5st Column 94 6st Column 92 7st Column 102 8st Column 79 9st Column 104 10st Column 84 2nd Row 1st Column Number of absences comma y 2nd Column 11 3rd Column 15 4st Column 18 5st Column 18 6st Column 16 7st Column 23 8st Column 12 9st Column 23 10st Column 13 EndTable 
 
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Part 1
A.
0.890
B.
0.980
C.
0.819
D.
0.881

The correlation coefficient \( r \) is approximately \( 0.980 \).

Thus, the correct answer is:
**B. 0.980**

Would you like more details or explanations on this calculation? 

Here are five related questions to deepen your understanding:
1. What does the correlation coefficient tell us about the relationship between two variables?
2. What are the limits of the correlation coefficient \( r \), and what do they represent?
3. How would you interpret a correlation of \( r = 0.980 \) in this context?
4. What are some factors that can affect the accuracy of the correlation coefficient?
5. Can correlation imply causation in this case?

**Tip:** A correlation close to \( 1 \) indicates a strong positive linear relationship between the two variables.

The data below are the temperatures on randomly chosen days during a summer class and the number of absences on those days. Calculate the correlation coefficient, r. Table: 1st Row (Temperatures, x): 76, 89, 95, 94, 92, 102, 79, 104, 84; 2nd Row (Number of absences, y): 11, 15, 18, 18, 16, 23, 12, 23, 13.

Learn how to calculate the Pearson correlation coefficient (r) using a dataset of temperatures and the number of absences during a summer class. The data provided shows the relationship between temperature and absences. This problem involves key statistical concepts and is suitable for students in Grades 10-12.

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