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Use the table to create a scatterplot. Determine the type of association. This table presents the x and y values required to cr...Asked by p
Use the table to create a scatterplot. Determine the type of association. This table presents the x and y values required to create scatter plots and determine the type of association. x 25 30 80 150 250 350 425 450 500 y 375 225 120 75 50 100 175 275 400 (1 point) Responses linear association linear association negative linear association negative linear association non-linear association non-linear association no association
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GPT-4o mini
To determine the type of association from the provided x and y values, let's first outline the pairs:
- (25, 375)
- (30, 225)
- (80, 120)
- (150, 75)
- (250, 50)
- (350, 100)
- (425, 175)
- (450, 275)
- (500, 400)
Next, let's analyze the trend of the data points:
1. **Initial Values (x=25 to x=150)**: As \( x \) increases from 25 to 150, \( y \) decreases significantly (from 375 to 75).
2. **Middle Values (x=150 to x=250)**: From 150 to 250, \( y \) continues to decrease, hitting a low at 50 for \( x=250 \).
3. **Later Values (x=250 to x=500)**: After reaching 250, when \( x \) increases from 250 to 500, \( y \) starts to increase (50 at x=250 to 400 at x=500).
This gives the impression of a more complex pattern rather than a straightforward linear relationship.
Based on the observations from the points presented:
- The initial decrease and later increase suggest a non-linear pattern, specifically it looks like a U-shaped curve.
Thus, you can conclude that the association is **non-linear**.
**Response:** non-linear association.
- (25, 375)
- (30, 225)
- (80, 120)
- (150, 75)
- (250, 50)
- (350, 100)
- (425, 175)
- (450, 275)
- (500, 400)
Next, let's analyze the trend of the data points:
1. **Initial Values (x=25 to x=150)**: As \( x \) increases from 25 to 150, \( y \) decreases significantly (from 375 to 75).
2. **Middle Values (x=150 to x=250)**: From 150 to 250, \( y \) continues to decrease, hitting a low at 50 for \( x=250 \).
3. **Later Values (x=250 to x=500)**: After reaching 250, when \( x \) increases from 250 to 500, \( y \) starts to increase (50 at x=250 to 400 at x=500).
This gives the impression of a more complex pattern rather than a straightforward linear relationship.
Based on the observations from the points presented:
- The initial decrease and later increase suggest a non-linear pattern, specifically it looks like a U-shaped curve.
Thus, you can conclude that the association is **non-linear**.
**Response:** non-linear association.
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