To match the scatter plots with their corresponding association descriptions, we will analyze the visual trends of the scatter plots:

Step-by-Step Matching:

1. Top-left scatter plot:

   • The points show a negative linear trend, meaning as one variable increases, the other decreases in a straight-line manner.
   • Match: Negative, linear association.

2. Top-middle scatter plot:

   • The points show a positive linear trend, meaning as one variable increases, the other also increases linearly.
   • Match: Positive, linear association.

3. Top-right scatter plot:

   • The points appear randomly scattered with no clear trend.
   • Match: No association.

4. Bottom-left scatter plot:

   • The points follow a negative nonlinear trend (a curve that decreases as one variable increases).
   • Match: Negative, nonlinear association.

5. Bottom-middle scatter plot:

   • The points show a positive nonlinear trend (a curve that increases as one variable increases).
   • Match: Positive, nonlinear association.

Final Answer:

1. Negative, linear association ↔ Top-left plot
2. Positive, linear association ↔ Top-middle plot
3. Negative, nonlinear association ↔ Bottom-left plot
4. No association ↔ Top-right plot
5. Positive, nonlinear association ↔ Bottom-middle plot

Would you like me to explain scatter plots or associations further? Here are 5 related questions:

1. What is the difference between linear and nonlinear associations?
2. Can you explain correlation coefficients and how they relate to scatter plots?
3. How do you determine if a scatter plot shows no association?
4. What are some real-life examples of negative and positive associations?
5. How can we measure the strength of an association in scatter plots?

Tip: In scatter plots, a "line of best fit" is often used to help identify trends. If the line is straight, the association is linear. If curved, it's nonlinear.