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 12 3rd Column 16 4st Column 19 5st Column 19 6st Column 17 7st Column 24 8st Column 13 9st Column 24 10st Column 14 EndTable 
 
Question content area bottom
Part 1
A.
0.890
B.
0.980
C.
0.819
D.
0.881

 numpy as np

# Given data
temperature = np.array([76, 89, 95, 94, 92, 102, 79, 104, 84])
absences = np.array([12, 16, 19, 19, 17, 24, 13, 24, 14])

# Calculate the correlation coefficient
correlation_coefficient = np.corrcoef(temperature, absences)[0, 1]
correlation_coefficientThe correlation coefficient, \( r \), is approximately \( 0.980 \). Therefore, the correct answer is:

**B. 0.980**

Would you like details about the calculation process or the interpretation of this result? 

Here are five follow-up questions:
1. How is the correlation coefficient interpreted in the context of this data?
2. What does a positive correlation coefficient signify in this case?
3. Can a high correlation coefficient imply causation between temperature and absences?
4. How could this analysis be visualized using a scatter plot?
5. What might be the limitations of using correlation in this scenario?

**Tip:** Always ensure that data is properly scaled and checked for outliers to avoid skewed correlation results.

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 12 3rd Column 16 4st Column 19 5st Column 19 6st Column 17 7st Column 24 8st Column 13 9st Column 24 10st Column 14 EndTable. Part 1 A. 0.890 B. 0.980 C. 0.819 D. 0.881

Learn how to calculate the correlation coefficient (r) between temperature and absences using statistical formulas. Example data includes temperatures (76°F, 89°F, etc.) and absences (12, 16, etc.). Suitable for students in Grades 10-12 studying statistics.

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Given data

Calculate the correlation coefficient

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