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

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon
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
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
± 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
Background image of page 2
Image of page 3
This is the end of the preview. Sign up to access the rest of the document.

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.

Page1 / 4

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

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online