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Correlation Quick Check 3 of 53 of 5 Items Question Use the tables to create scatterplots and determine which scatterplot shows...Question
Correlation Quick Check
4 of 54 of 5 Items
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Use the tables to create scatterplots and determine which scatterplot shows a linear association.(1 point)
Responses
x 5 8 2 4 6 12 10 1 3 7
y 800 700 350 450 500 1000 900 250 400 750
x 5 8 2 4 6 12 10 1 3 7 y 800 700 350 450 500 1000 900 250 400 750
x 5 8 2 4 6 12 10 1 3 7
y 550 800 250 300 700 1000 650 300 100 700
x 5 8 2 4 6 12 10 1 3 7 y 550 800 250 300 700 1000 650 300 100 700
x 5 8 2 4 6 12 10 1 3 7
y 550 800 350 450 700 1000 900 250 400 750
x 5 8 2 4 6 12 10 1 3 7 y 550 800 350 450 700 1000 900 250 400 750
x 5 8 2 4 6 12 10 1 3 7
y 550 800 250 500 500 400 650 150 400 750
Answers
Answered by
GPT-4o mini
To determine which scatterplot shows a linear association, we should look for a pattern where the points roughly form a straight line, whether ascending or descending.
Let's analyze each of the datasets:
1. **Scatterplot 1:**
- Points: (5, 800), (8, 700), (2, 350), (4, 450), (6, 500), (12, 1000), (10, 900), (1, 250), (3, 400), (7, 750)
- This dataset shows a general downward trend in the y-values as x increases, but there are some points that deviate significantly (around (12, 1000)), which makes it less linear.
2. **Scatterplot 2:**
- Points: (5, 550), (8, 800), (2, 250), (4, 300), (6, 700), (12, 1000), (10, 650), (1, 300), (3, 100), (7, 700)
- This dataset seems to have varying trends, and some points do not form a linear pattern, especially the point (1, 300) and (3, 100).
3. **Scatterplot 3:**
- Points: (5, 550), (8, 800), (2, 350), (4, 450), (6, 700), (12, 1000), (10, 900), (1, 250), (3, 400), (7, 750)
- Similar to the previous data, it does not present a strong linear association.
4. **Scatterplot 4:**
- Points: (5, 550), (8, 800), (2, 250), (4, 500), (6, 500), (12, 400), (10, 650), (1, 150), (3, 400), (7, 750)
- This scatterplot shows more random variability without a clear linear trend.
**Conclusion:**
None of the scatterplots exhibit a clear linear association, however, based on the observations:
- If forced to choose, Scatterplot 1 shows the closest to a linear association trend (despite the outlier) because most other plots have more variability and less directionality in their trends.
To make a more informed decision, it's essential to visually examine actual scatterplots based on the provided data points. If visual representation is available, it would significantly aid in this analysis.
Let's analyze each of the datasets:
1. **Scatterplot 1:**
- Points: (5, 800), (8, 700), (2, 350), (4, 450), (6, 500), (12, 1000), (10, 900), (1, 250), (3, 400), (7, 750)
- This dataset shows a general downward trend in the y-values as x increases, but there are some points that deviate significantly (around (12, 1000)), which makes it less linear.
2. **Scatterplot 2:**
- Points: (5, 550), (8, 800), (2, 250), (4, 300), (6, 700), (12, 1000), (10, 650), (1, 300), (3, 100), (7, 700)
- This dataset seems to have varying trends, and some points do not form a linear pattern, especially the point (1, 300) and (3, 100).
3. **Scatterplot 3:**
- Points: (5, 550), (8, 800), (2, 350), (4, 450), (6, 700), (12, 1000), (10, 900), (1, 250), (3, 400), (7, 750)
- Similar to the previous data, it does not present a strong linear association.
4. **Scatterplot 4:**
- Points: (5, 550), (8, 800), (2, 250), (4, 500), (6, 500), (12, 400), (10, 650), (1, 150), (3, 400), (7, 750)
- This scatterplot shows more random variability without a clear linear trend.
**Conclusion:**
None of the scatterplots exhibit a clear linear association, however, based on the observations:
- If forced to choose, Scatterplot 1 shows the closest to a linear association trend (despite the outlier) because most other plots have more variability and less directionality in their trends.
To make a more informed decision, it's essential to visually examine actual scatterplots based on the provided data points. If visual representation is available, it would significantly aid in this analysis.
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