To match the linear correlation coefficient (r) to the correct scatter diagram, let’s analyze what each value means in terms of the graph:

• r = -0.933: This represents a strong negative linear correlation. As the value of x increases, the value of y decreases consistently in a nearly straight line. The points will appear to follow a downward slope from left to right, and the relationship between x and y will be very consistent.

• r = 0.523: This represents a moderate positive linear correlation. As x increases, y also increases, but not in a perfectly straight line. The scatter will have some spread around a general upward slope.

• r = 0.038: This represents a very weak positive correlation. The points will be scattered with no clear trend, and there will be minimal upward or downward slope.

• r = 1: This represents a perfect positive linear correlation. The points will all fall exactly on a straight line that slants upward from left to right.

Now, to identify which scatter diagram corresponds to r = -0.933, look for the following features:

• A strong downward slope, with points tightly packed around a straight line, and very few outliers.

Would you like me to further explain the visual characteristics of each case or help interpret the scatter diagrams once you can access them?

If you have them available, feel free to upload the scatter diagrams for me to match them with the correlation coefficients.

Related Questions:

1. What does a correlation coefficient close to 0 mean for a scatter plot?
2. How do you calculate the correlation coefficient from data points?
3. How does a correlation of r = -1 look compared to r = 0?
4. Can a correlation coefficient be negative in real-world data? Provide examples.
5. What’s the difference between positive and negative correlation in practical terms?

Tip: When looking for correlation in a scatter plot, visualize whether the points are generally moving in the same or opposite direction to determine the sign of the correlation.