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06 regression

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06 regression

1. 1. 1 ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Regression/Curve Fitting ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Objectives • Understanding the difference between regression and interpolation • Knowing how to “best fit” a polynomial into a set of data • Knowing how to use a polynomial to interpolate data
2. 2. 2 ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Measured Data ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Polynomial Fit!
3. 3. 3 ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Line Fit! ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Which is better?
4. 4. 4 ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Curve Fitting • If the data measured is of high accuracy and it is required to estimate the values of the function between the given points, then, polynomial interpolation is the best choice. • If the measurements are expected to be of low accuracy, or the number of measured points is too large, regression would be the best choice. ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Regression
5. 5. 5 ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Why Regression? • Measurements that we get from real situations are not usually consistent! • The number of “pieces” of information that we can get about a certain project is HUGE • You can NEVER measure exact values! ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Measured Data
6. 6. 6 ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik But, how to get the equation of a line that is “good” for all the data you have! ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Equation of a Line: Revision xaay 10 += If you have two points 1101 xaay += 2102 xaay +=       =             2 1 1 0 2 1 1 1 y y a a x x
7. 7. 7 ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Solving for the constants! 12 12 1 12 2112 0 & xx yy a xx yxyx a − − = − − = ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik What if I have more than two points?
8. 8. 8 ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik For every point nn xaay xaay xaay 10 2102 1101 +≠ +≠ +≠ M                   ≠               1 02 1 2 1 1 1 1 a a x x x y y y nn MMM ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik So, we may write the error vector                   −               =               1 02 1 2 1 2 1 1 1 1 a a x x x y y y e e e nnn MMMM { } { } [ ] { } 1*22*1*1* aAye nnn −=
9. 9. 9 ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Square the error { } { } [ ]{ }aAye −= { } { } { } { } { } [ ]{ } { } [ ] { } { } [ ] [ ]{ } 2 eaAAayAa aAyyyee TTTT TTT =+− −= ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Note: this is a scalar equation! { } { } { } { } { } [ ]{ } { } [ ] { } { } [ ] [ ]{ } 2 eaAAayAa aAyyyee TTTT TTT =+− −= { } [ ]{ } { } [ ] { }yAaaAy TTT = { } { } { } [ ] { } { } [ ] [ ]{ }aAAayAayye TTTTT +−= 2 2
10. 10. 10 ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Note: this is a quadratic equation in {a}!!! { } { } { } [ ] { } { } [ ] [ ]{ }aAAayAayye TTTTT +−= 2 2 To minimize the error in the above equation, we need to differentiate with respect to the parameters { } [ ] { } [ ] [ ]{ } 022 2 =+−= aAAyA ad ed TT ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Solving the equation We get: { } [ ] { } [ ] [ ]{ } 022 2 =+−= aAAyA ad ed TT [ ] [ ] { } [ ] { } 1**21*22**2 n T nn T n yAaAA = [ ] { } { } 1*21*22*2 yaA = { } [ ] { }yAa 1− =
11. 11. 11 ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Example • If you are given the data. • Find the equation of the “best-fit” line. y=a1+a2x 5.57 66 3.55 44 23 2.52 0.51 yx ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Solution                       =                             5.5 6 5.3 4 2 5.2 5.0 71 61 51 41 31 21 11 1 0 a a [ ] { }                       =                       = 5.5 6 5.3 4 2 5.2 5.0 & 71 61 51 41 31 21 11 yA
12. 12. 12 ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Solution [ ] [ ]       =                             = 14028 287 71 61 51 41 31 21 11 7654321 1111111 AA T ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Solution [ ] { }       =                             = 5.119 24 5.5 6 5.3 4 2 5.2 5.0 7654321 1111111 yA T
13. 13. 13 ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Solution       =             5.119 24 14028 287 1 0 a a [ ] [ ]{ } [ ] { }yAaAA TT =       =       8393.0 0714.0 1 0 a a 0714.08393.0 += xy ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Example • If you are given the data. • Find the equation of the “best-fit” parabola. y=a0+a1x+a2x2 5.57 66 3.55 44 23 2.52 0.51 yx
14. 14. 14 ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Solution                       =                                 5.5 6 5.3 4 2 5.2 5.0 49 36 25 16 9 4 1 71 61 51 41 31 21 11 2 1 0 a a a [ ] { }                       =                       = 5.5 6 5.3 4 2 5.2 5.0 & 49 36 25 16 9 4 1 71 61 51 41 31 21 11 yA ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Solution [ ] [ ]           =                                 = 4676784140 78414028 140287 49 36 25 16 9 4 1 71 61 51 41 31 21 11 49362516941 7654321 1111111 AA T
15. 15. 15 ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Solution [ ] { }           =                                 = 5.665 5.119 24 5.5 6 5.3 4 2 5.2 5.0 49362516941 7654321 1111111 yA T ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Solution [ ] [ ]{ } [ ] { }yAaAA TT =           − − =           0298.0 0774.1 2857.0 2 1 0 a a a 2857.00774.10298.0 2 −+−= xxy
16. 16. 16 ENEM602 Spring 2007 Dr. Eng. Mohammad Tawfik Homework #4 • Chapter 17, pp. 471-472, numbers: 17.4,17.5. • Use the data and regression to get the equation of the line that best fits the data • Number 17.7 • Use the data and regression to get the equation of the line and the parabola that best fit the data