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Here are the results for the different models fitted to the dataHere are the results for the different models fitted to the 

1. **Linear Model**: \( y = 2.5x + 0.2 \)
2. **Parabolic Model**: \( y = -0.036x^2 + 2.93x - 0.8 \)
3. **Power Model**: \( y = 2.67x^{0.97} \)
4. **Saturating Growth Curve Model**: \( y = \frac{471.53x}{178.42 + x} \)

In the plot, each model is shown alongside the data points to compare how well each model fits. Based on visual inspection, the straight line and power model seem to be closely aligned with the data. However, further statistical evaluation such as R-squared values could be used to quantitatively assess the best fit.

Do you want more details on a specific model or the next steps in the analysis?

### Relative Questions:
1. How is the least-squares method used for model fitting?
2. What is the significance of the power model in data fitting?
3. Can you explain how to interpret the parameters in a parabolic fit?
4. How do you compute R-squared to assess model quality?
5. What are some real-world applications of the saturating growth model?

**Tip**: When fitting multiple models, always check for overfitting, particularly with more complex models like parabolas. Simpler models may generalize better.

Given the following data, use least-squares regression to fit the models: (a) A straight line, (b) A parabolic equation, (c) A power equation, (d) A saturating-growth-curve equation. Plot the data along with all the curves. Do any of the curves appear to fit the data? If so, justify.

Learn how to use least-squares regression to fit different models (linear, parabolic, power, and saturating growth curve) to a dataset. Includes step-by-step calculations, formulas, and a visual comparison of how well each model fits the data.

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