The quadratic function that best fits the given data is:

$f(x) = 99.84x^2 + 1.79x - 1.57$

Would you like further details on the method used here or have any questions?

Here are some related questions to deepen understanding:

1. How do we determine the degree of a polynomial that best fits a given data set?
2. What is the significance of each coefficient in a quadratic function?
3. How does one interpret the results of a least-squares regression in practical terms?
4. Can a higher-degree polynomial provide a better fit for these data points, and how would we assess this?
5. How does rounding affect the accuracy of a fitted quadratic function?

Tip: When rounding coefficients in fitted models, always keep in mind the potential impact on accuracy, especially in sensitive applications.