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The question asks for the best approximation of the form \( y(x) = a + b(x^2 - x) \) to fit the given data points:

\[
(-1, 1),\ (0, -1),\ (1, 3),\ (2, -5)
\]

We need to determine the values of \(a\) and \(b\). Here's how to approach the problem:

### Step 1: Set up the system of equations

For each data point \((x_i, y_i)\), substitute into the equation \( y(x) = a + b(x^2 - x) \). This will generate a system of linear equations:

1. For \((-1, 1)\):
   \[
   1 = a + b((-1)^2 - (-1)) = a + b(1 + 1) = a + 2b
   \]

2. For \((0, -1)\):
   \[
   -1 = a + b(0^2 - 0) = a
   \]

3. For \((1, 3)\):
   \[
   3 = a + b(1^2 - 1) = a + b(1 - 1) = a
   \]

4. For \((2, -5)\):
   \[
   -5 = a + b(2^2 - 2) = a + b(4 - 2) = a + 2b
   \]

### Step 2: Solve the system of equations

From equation 2 and equation 3, we find:
\[
a = -1 \quad \text{and} \quad a = 3
\]
This implies that there is no consistent solution for \(a\), because these two values of \(a\) are contradictory.

Let me review the details and calculations again or check if there’s any oversight. Would you like me to proceed with more clarifications, or attempt the problem another way?

Feel free to ask for details or raise any questions!

### Follow-up Questions:
1. Would you like me to explain the linearization process more deeply?
2. Do you want to check for any alternative error-minimizing methods?
3. Should I review the possibility of an error in the equations?
4. Are you interested in seeing how this problem connects to least-squares approximation?
5. Would you like to explore similar questions involving different functional forms?

### Tip:
Always carefully double-check your substitution and system setup, as missing details in complex data fitting problems can lead to unexpected contradictions!

Find the best approximation of the form y(x) = a + b(x^2 - x) to the data set (-1, 1), (0, -1), (1, 3), (2, -5). What are a and b?

Find the best fit approximation using the form y(x) = a + b(x² - x) for the data points (-1,1), (0,-1), (1,3), and (2,-5). This problem covers systems of linear equations and introduces least squares approximation concepts.

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