A polynomial is a best polynomial least-squares approximation when it minimizes the weighted error norm over all polynomials of degree at most :
Equivalently it minimizes . The weight is integrable, so the expression is finite for continuous functions; it defines a genuine norm because a continuous function with zero weighted squared integral must be identically zero.
Take any . Write . The second term belongs to , so the assumed orthogonality gives . Expanding the squared norm yields the Pythagorean theorem in this inner product:
Taking square roots proves
Equality can hold only when , hence , so the best polynomial is unique. Completeness of the ambient space is unnecessary for this orthogonality argument.
Write . It suffices to impose orthogonality of the error to the basis of . With the printed weight and , the normal equations are
Here, for example, and . Solving gives , . Therefore
The two normal equations make the error orthogonal to every linear polynomial, and part (b) then proves optimality, rather than merely furnishing a candidate fit.
The leading error term can be removed by comparing the two step sizes and :
Multiply the second expression by four and subtract the first. The terms cancel, so the first Richardson extrapolation step is
Repeat with and , now cancelling the fourth-order error using the factor . In general, with ,
Under the stated even-power expansion, successive columns have errors of orders . Each step uses the known scaling of the leading error to eliminate its unknown coefficient.
For one interval, the trapezoidal rule gives
For two intervals of width , the composite trapezoidal rule gives half weights at the endpoints and full weight at the internal node:
Apply the first Richardson extrapolation step to the two computed approximations:
The exact integral is , so the actual absolute error is
Thus the requested strict error bound is verified directly. This first extrapolation of trapezoidal approximations is also the one-panel Simpson's rule value on the three nodes .

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