To determine whether the given data fits a quadratic or exponential model better, let's analyze the data for both patterns.

Quadratic Model Analysis

For a quadratic model $Y = aX^2 + bX + c$, we can check the second differences of the $Y$ values. The second differences for a quadratic function should be constant.

Given data points: $X = [5, 10, 15, 20]$ $Y = [88.574, 52.302, 30.884, 18.236]$

First, calculate the first differences ($\Delta Y$): $\Delta Y_1 = 52.302 - 88.574 = -36.272$ $\Delta Y_2 = 30.884 - 52.302 = -21.418$ $\Delta Y_3 = 18.236 - 30.884 = -12.648$

Next, calculate the second differences ($\Delta^2 Y$): $\Delta^2 Y_1 = -21.418 - (-36.272) = 14.854$ $\Delta^2 Y_2 = -12.648 - (-21.418) = 8.77$

The second differences are not constant, suggesting that a quadratic model might not be the best fit.

Exponential Model Analysis

For an exponential model $Y = A e^{bX}$, we should look for a common multiplier in the ratios of successive $Y$ values.

Calculate the ratios of successive $Y$ values: $\frac{Y_2}{Y_1} = \frac{52.302}{88.574} \approx 0.5906$ $\frac{Y_3}{Y_2} = \frac{30.884}{52.302} \approx 0.5906$ $\frac{Y_4}{Y_3} = \frac{18.236}{30.884} \approx 0.5906$

The ratios are approximately constant, indicating that an exponential model might be a better fit.

Conclusion

Based on the analysis:

• The second differences for a quadratic model are not constant, suggesting it might not be the best fit.
• The ratios of successive $Y$ values are approximately constant, suggesting an exponential model is likely a better fit.

The common multiplier for the exponential model is approximately $0.5906$.

To summarize:

• Quadratic Model: Second differences are not constant.
• Exponential Model: Common multiplier is $\approx 0.5906$.

If you have any questions or need further details, please let me know.

Here are some related questions to further your understanding:

1. How can we formally test the goodness of fit for quadratic and exponential models?
2. What are the implications of choosing an exponential model over a quadratic one?
3. Can we use logarithmic transformations to linearize exponential data?
4. What are some real-world examples of phenomena best modeled by quadratic functions?
5. What are some real-world examples of phenomena best modeled by exponential functions?
6. How does the presence of noise in data affect model selection?
7. What are the limitations of using polynomial and exponential models?
8. How can we apply regression analysis to predict future values?

Tip: Always verify the fit of your chosen model using residual plots and goodness-of-fit statistics to ensure it accurately captures the data's underlying trend.