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Interpolation methods, specifically focusing on newton's divided difference formula and gregory-newton forward interpolation. These methods are used to estimate new data points based on a discrete set of known data points. Examples and formulas for deriving third order polynomials and interpolating unknown values.
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Interpolation In the mathematical field of numerical analysis, interpolation is a type of estimation, a method of constructing (finding) new data points based on the range of a discrete set of known data points.
Newton’s Divided Difference Formula
Newton’s Divided Difference Formula F(g/min) (^3 5 6 ) P (mV) (^293 508 585 ) Derive a third order polynomial passing through these data points.
Newton’s Divided Difference Formula To get the nth order interpolating polynomial: p n (x) = f 0
(x-x n- 1 )f 01234…n
Newton’s Divided Difference Formula x f(x) f[x, x]^1 f[x, x, x]^2 f[x,^ x, x, x]^3 3 293
5 508 - 10. 77.00 0. 6 585 - 4.
9 764 Here, x ≡ F and f(x) ≡ P.
Gregory-Newton Forward Interpolation This method uses formula Where f 0 is the first value of the data set, ∆f 0 , ∆ 2 f 0 , ∆ 3 f 0 are the first, second and third forward differences respectively. The variable r is the difference between an unknown point x and a known point x 1 divided by the interval h, that is, 0 0
Gregory-Newton Forward Interpolation Example: Use the Gregory-Newton forward interpolation to compute f(1.03) from the following data. Note: The formula of is valid only when the values are equally spaced with interval.
Gregory-Newton Forward Interpolation Solution: Answer: 1. 0