Lecture26-27

# Lecture26-27 - Function Approximation ◗ Function...

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Unformatted text preview: Function Approximation ◗ Function approximation (Chapters 13 & 14)-- method of least squares-- minimize the residuals-- given data of points have noises-- the purpose is to find the trend represented by data. ◗ Function interpolation (Chapters 15 & 16)-- approximating function match the given data exactly-- given data of points are precise-- the purpose is to find data between these points Interpolation and Regression Chapter 13 Curve Fitting: Fitting a Straight Line Least Square Regression ◗ Curve Fitting ◗ Statistics Review ◗ Linear Least Square Regression ◗ Linearization of Nonlinear Relationships ◗ MATLAB Functions Wind Tunnel Experiment Measure air resistance as a function of velocity Curve fitting ( a ) Least-squares regression ( b ) Linear interpolation ( c ) Curvilinear interpolation Regression and Interpolation Curve fitting Least-squares fit of a straight line 1450 830 1220 610 550 380 70 25 80 70 60 50 40 30 20 10 v , m/s F , N Simple Statistics Measurement of the coefficient of thermal expansion of structural steel [ × 10- 6 in/(in ⋅ ° F)] 535 6 667 6 670 6 592 6 633 6 627 6 598 6 499 6 621 6 445 6 451 6 403 6 703 6 542 6 659 6 624 6 733 6 662 6 721...
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## This note was uploaded on 09/05/2011 for the course EGM 3344 taught by Professor Raphaelhaftka during the Spring '09 term at University of Florida.

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Lecture26-27 - Function Approximation ◗ Function...

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