To determine which model best fits the data represented by the points (where each point corresponds to the value of a basketball card over time), we need to consider the characteristics of each model.
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Linear Model: A linear model would imply that the card's value increases or decreases by a constant amount each year. This would be represented as a straight line on a graph.
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Quadratic Model: A quadratic model would suggest that the value of the card changes at an increasing or decreasing rate, which would result in a parabolic shape on the graph.
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Exponential Model: An exponential model would indicate that the value of the card increases or decreases by a percentage of its current value each year, leading to a curve that grows (or decays) rapidly over time.
Given your description, you would need to assess how the values change as the years increase:
- If the values appear to change uniformly over time, a linear model might fit.
- If there are signs of increasing or decreasing acceleration in the values, a quadratic model might be appropriate.
- If the values seem to grow or shrink rapidly, an exponential model could be the best fit.
Without seeing the actual graph or data points, it's challenging to provide a definitive answer. However, if the basketball card values are increasing rapidly or at a varying rate, then an exponential model might be the best fit. If you notice a steady increase, then a linear model could be appropriate. If the increase in values accelerates, then a quadratic model might be the most suitable choice.
Please review the data or the graph again based on these descriptions to see which model aligns best with the observed trends.
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