Asked by turtleslol
If these are the points on a scatterplot, is it a nonlinear?
X: 15, 35, 100, 80, 110, 60, 90, 50, 25, 125
Y: 100, 150, 350, 250, 375, 225, 300, 200, 125, 425
X: 15, 35, 100, 80, 110, 60, 90, 50, 25, 125
Y: 100, 150, 350, 250, 375, 225, 300, 200, 125, 425
Answers
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Answered by
GPT-4o mini
To determine whether the relationship between the given X and Y points is nonlinear, we can look at the pattern of the points when plotted on a scatterplot. While I cannot create a scatterplot directly, I can help you analyze the values.
Here are the given points:
- (15, 100)
- (35, 150)
- (100, 350)
- (80, 250)
- (110, 375)
- (60, 225)
- (90, 300)
- (50, 200)
- (25, 125)
- (125, 425)
**Step 1: Examine the trend.**
To check for linearity, we would typically look to see if the points seem to fall along a straight line. However, with these data points, you can observe that the Y values increase irregularly as X increases.
**Step 2: Look for curves:**
Given the progression of Y values, particularly noting that the increase in Y isn't consistent (e.g., moving from 100 to 150 increases by 50, but moving from 100 to 350 increases by 250), this suggests a nonlinear trend.
**Step 3: Consider mathematical models:**
If you were to apply a curve fitting technique (like polynomial regression), you might find that a polynomial or other nonlinear model fits these points better than a simple linear model.
**Conclusion:**
Based on the progression and distribution of the points, it is likely that the plot of these points would demonstrate a nonlinear relationship. To confirm definitively, you could create a scatterplot and try fitting both linear and nonlinear models to see which provides a better fit.
Here are the given points:
- (15, 100)
- (35, 150)
- (100, 350)
- (80, 250)
- (110, 375)
- (60, 225)
- (90, 300)
- (50, 200)
- (25, 125)
- (125, 425)
**Step 1: Examine the trend.**
To check for linearity, we would typically look to see if the points seem to fall along a straight line. However, with these data points, you can observe that the Y values increase irregularly as X increases.
**Step 2: Look for curves:**
Given the progression of Y values, particularly noting that the increase in Y isn't consistent (e.g., moving from 100 to 150 increases by 50, but moving from 100 to 350 increases by 250), this suggests a nonlinear trend.
**Step 3: Consider mathematical models:**
If you were to apply a curve fitting technique (like polynomial regression), you might find that a polynomial or other nonlinear model fits these points better than a simple linear model.
**Conclusion:**
Based on the progression and distribution of the points, it is likely that the plot of these points would demonstrate a nonlinear relationship. To confirm definitively, you could create a scatterplot and try fitting both linear and nonlinear models to see which provides a better fit.
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