The <math>w_i</math>:s are called weights and are derived from computing the corresponding Lagrange polynomials thusly:
Here the <math>w_i</math>:s are fix constants depending only on the chosen <math>x_i</math>:s. Computing this integral for different numbers of points <math>n</math> will yield some famous quadrature formulas:
| <math>n</math> | Common name | Short form | Exact for | 
| 0 | Midpoint rule | <math>M(x)</math> | <math>f \in \pi_1</math> | 
| 1 | Trapezoid rule | <math>T(x)</math> | <math>f \in \pi_1</math> | 
| 2 | Simpson's rule | <math>S(x)</math> | <math>f \in \pi_3</math> | 
| 3 | ? | ? | <math>f \in \pi_3</math> | 
| 4 | Bode's rule[?] | ? | <math>f \in \pi_5</math> | 
| ... | ... | ... | ... | 
These are the actual Newton-Cotes formulas. The <math>\pi</math>:s on the left signify on which monomial[?] bases the solution is exact, e.g. a solution that is exact for <math>\pi_2</math> will be exact on the monomials in the set <math>\{1, x, x^2\}</math>. Note that the formulas' exactness increase in odd steps, so using the trapezoid rule for integrating is actually no better than using the midpoint rule, and just a waste of time. In the same manner, Simpson's rule will be exact for monomials in <math>\{1, x, x^2, x^3\}</math> which will suffice for most functions found in practice.
If you also let the distance <math>h</math> between each sample point vary, you obtain another group of quadrature formulas, the best of which is know as Gaussian quadrature.
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