# Lecture22 - Engineering Analysis ENG 3420 Fall 2009 Dan C...

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Engineering Analysis ENG 3420 Fall 2009 Dan C. Marinescu Office: HEC 439 B Office hours: Tu-Th 11:00-12:00

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2 Lecture 22 Lecture 22 ± Attention: The last homework HW5 and the last project are due on Tuesday November 24!! ± Last time: ² Linear regression ² Exponential, power, and saturation non-linear models ² Linear least squares regression ± Today ² Linear regression versus sample mean. Coefficient of determination ² Polynomial least squares fit ² Multiple linear regression ² General linear squares ² More on non-linear models ² Interpolation (Chapter 15) ± Polynomial interpolation ± Newton interpolating polynomials ± Lagrange interpolating polynomials ± Next Time ² Splines
Quantification of Errors ± For a straight line the sum of the squares of the estimate residuals is: ± The standard error of the estimate: s y / x = S r n 2 S r = e i 2 i = 1 n = y i a 0 a 1 x i () 2 i = 1 n

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Linear regression versus the sample mean ± What is the difference between linear regression and the case when we simply compute the sample mean and draw a line corresponding to the sample mean? ± The spread Æ the histogram of the differences between the values predicted by linear regression and the actual sample values. ± Regression data showing (a) the spread of data around the mean of the dependent data and (b) the spread of the data around the best fit line: ± The reduction in spread represents the improvement due to linear regression.
Coefficient of Determination ± The coefficient of determination r 2 Æ ± r 2 represents the percentage of the original uncertainty explained by the model. ± For a perfect fit, S r =0 and r 2 =1. ± If r 2 =0, there is no improvement over simply picking the mean. ± If r 2 <0, the model is worse than simply picking the mean! r 2 = S t S r S t ( ) = = n i i t y y S 1 2

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Example V (m/s) F (N) (y i -a 0 -a 1 x i ) 2 4171 7245 911 30 16699 81837 89180 16044 216118 ix i y i a 0 +a 1 x i (y i - ȳ ) 2 1 10 25 -39.58 380535 2 20 70 155.12 327041 3 30 380 349.82 68579 4 40 550 544.52 8441 5 50 610 739.23 1016 6 60 1220 933.93 334229 7 70 830 1128.63 35391 8 80 1450 1323.33 653066 Σ 360 5135 1808297 F est = − 234.2857 + 19.47024 v S t = y i y () 2 = 1808297 S r = y i a 0 a 1 x i 2 = 216118 s y = 1808297 8 1 = 508.26 s y / x = 216118 8 2 = 189.79 r 2 = 1808297 216118 1808297 = 0.8805 88.05% of the original uncertainty has been explained by the linear model
Polynomial least-fit squares ± MATLAB has a built-in function polyfit that fits a least-squares n-th order polynomial to data: ² p = polyfit( x , y , n ) ± x : independent data ± y : dependent data ± n : order of polynomial to fit ± p : coefficients of polynomial f ( x )= p 1 x n + p 2 x n -1 +…+ p n x + p n +1 ± MATLAB’s polyval command can be used to compute a value using the coefficients.

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## This note was uploaded on 02/17/2012 for the course EGN 3420 taught by Professor Staff during the Spring '08 term at University of Central Florida.

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Lecture22 - Engineering Analysis ENG 3420 Fall 2009 Dan C...

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