Match the correlation coefficients with the scatterplots shown below.
Scatterplot
Correlation coefficient
\[\greenD{\text{Scatterplot A}}\]
\[\maroonD{\text{Scatterplot B}}\]
\[\goldD{\text{Scatterplot C}}\]
\[\blueD{\text{Scatterplot D}}\]
\[r=0.65\]
\[r=0.84\]
\[r=-0.02\]
\[r=-0.72\]
\[\greenD{\text{Scatterplot A}}\]	
\[\maroonD{\text{Scatterplot B}}\]
Scatterplot A. The scatterplot has horizontal axis, x, which ranges from 0 to 9, in increments of 1, and vertical axis, y, which ranges from 0 to 9, in increments of 1. 19 points fall in a very wide diagonal pattern between (1.1, 5.7) and (8.3, 0). Outliers exist. All values estimated.
\[\small{1}\]
\[\small{2}\]
\[\small{3}\]
\[\small{4}\]
\[\small{5}\]
\[\small{6}\]
\[\small{7}\]
\[\small{8}\]
\[\small{9}\]
\[\small{1}\]
\[\small{2}\]
\[\small{3}\]
\[\small{4}\]
\[\small{5}\]
\[\small{6}\]
\[\small{7}\]
\[\small{8}\]
\[\small{9}\]
\[y\]
\[x\]
Scatterplot B. The scatterplot has horizontal axis, x, which ranges from 0 to 9, in increments of 1, and vertical axis, y, which ranges from 0 to 9, in increments of 1. 19 points rise diagonally in a tight narrow cluster between (0.1, 4.1) and (8.75, 9.1). Outliers exist. All values estimated.
\[\small{1}\]
\[\small{2}\]
\[\small{3}\]
\[\small{4}\]
\[\small{5}\]
\[\small{6}\]
\[\small{7}\]
\[\small{8}\]
\[\small{9}\]
\[\small{1}\]
\[\small{2}\]
\[\small{3}\]
\[\small{4}\]
\[\small{5}\]
\[\small{6}\]
\[\small{7}\]
\[\small{8}\]
\[\small{9}\]
\[y\]
\[x\]


To match the correlation coefficients \( r \) with the scatterplots, we need to evaluate how closely the points fit a line and whether the trend is positive, negative, or neutral. Here's how we can analyze the scatterplots based on their descriptions:

1. **Scatterplot A**:
   - The points fall in a **wide diagonal pattern** from approximately (1.1, 5.7) to (8.3, 0), indicating a **negative trend**.
   - The points are spread out, not tightly clustered, but there is a clear negative direction.
   - This suggests a **moderately negative correlation**.
   - **Best match**: \( r = -0.72 \) (moderate negative correlation).

2. **Scatterplot B**:
   - The points rise diagonally in a **tight narrow cluster** from approximately (0.1, 4.1) to (8.75, 9.1), indicating a **strong positive trend**.
   - The tight clustering suggests a **strong positive correlation**.
   - **Best match**: \( r = 0.84 \) (strong positive correlation).

### Remaining Scatterplots and Correlations:
Without the descriptions of the remaining scatterplots C and D, we would assign the remaining correlation coefficients based on general scatterplot patterns:
- \( r = 0.65 \) likely corresponds to a **moderate positive trend**.
- \( r = -0.02 \) would correspond to a **random scatter** with almost no correlation.

Would you like details on how to match the rest? 

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### Related Questions:
1. What is the difference between a strong and weak correlation?
2. How do positive and negative correlations affect data interpretation?
3. Can a scatterplot have outliers and still show a strong correlation?
4. What does a correlation of \( r = 0 \) signify?
5. How is the correlation coefficient \( r \) calculated from data points?

### Tip:
Correlation does not imply causation—always be cautious when interpreting relationships between variables.

Match the correlation coefficients with the scatterplots shown below. Scatterplot A: 19 points in a wide diagonal pattern, negative trend; Scatterplot B: 19 points in a tight diagonal cluster, positive trend. Correlation coefficients: r=0.65, r=0.84, r=-0.02, r=-0.72.

Learn how to match correlation coefficients with scatterplots. Analyze scatterplot A with a wide diagonal pattern and negative trend, and scatterplot B with a tight diagonal cluster and positive trend. Includes correlation coefficients: r=0.65, r=0.84, r=-0.02, r=-0.72. Ideal for students in Grades 10-12.

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