# Numerical - Linear Interpolation (chap 2) Some Numerical...

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Some Numerical Methods (1) Linear Interpolation (chap 2) ± Obtain a value at point b, where a<b<c )] ( ) ( [ ) ( ) ( a f c f a c a b a f b f + = Linear Modeling (Chap 4) ± Given values: a set of data points ± Linear Modeling (Also called linear regression): A process that determines the linear equation that is the best fit to a set of data points in terms of minimizing the sum of the squared distances between the line and the data points. ± Advantage: estimate the points for which we had no data ± Application: when the set of data roughly follows a linear model d3 d4 d5 d6

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± Procedures in finding a linear equation: Define a general equation: y = mx + b; Calculate the sum of the squared distances between the given data points and the general equation Compute the derivatives of the equation with respect to m and b and set the derivatives to zero. Roots of Polynomials (Chap5) ± F(x) = ± Two example methods: Incremental-Search Technique Newton-Raphson method
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## This note was uploaded on 02/19/2012 for the course PHY MPDESHPAND taught by Professor Abhaydeshpande during the Spring '09 term at SUNY Stony Brook.

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Numerical - Linear Interpolation (chap 2) Some Numerical...

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