 There is 

▼
 
a weak positive linear association

a strong negative linear association

little to no linear association

a strong positive linear association

a weak negative linear association
 
between the two variables. 

Part 3

**c.** The given graph is 

▼
 
not consistent

consistent
 
with the interpretation found in part​ (b), because the​ y-values appear to 

▼
 
increase

stay the same

decrease
 
as the​ x-values increase, and the data points appear 

▼
 
widely scattered about the regression line.

essentially scattered about a horizontal line.

clustered closely about the regression line.

### Part 2:
Given that the correlation coefficient \( r \) is approximately \(-0.957\), this indicates:

- **a strong negative linear association** between the two variables.

### Part 3:
The given graph should be:

- **consistent** with the interpretation found in part **b**, because the **y-values appear to decrease** as the **x-values increase**, and the data points appear **clustered closely about the regression line**.

This description aligns with the strong negative linear association found in the data.

Would you like to dive deeper into this topic, or do you have any other questions?

### Related Questions
1. What other types of associations can exist between two variables?
2. How does one visually interpret the strength of a correlation on a scatterplot?
3. How might the interpretation change if the correlation coefficient were close to zero?
4. What are some real-life examples of strong negative correlations?
5. Can non-linear relationships be analyzed using a correlation coefficient?
6. How would the regression line be calculated for this data set?
7. What is the role of outliers in determining the strength of correlation?
8. How can residuals help assess the fit of a regression model?

**Tip:** When analyzing scatterplots, always consider both the direction (positive or negative) and the strength (weak or strong) of the association between the variables.

Explore the concept of strong negative linear association in scatterplots. Learn how correlation coefficients indicate this relationship, with detailed analysis on how y-values decrease as x-values increase. Suitable for students and analysts studying data correlations.

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