ECOR 2606 - Lecture 16

# andb log uselinearregressiontofindbesta

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Unformatted text preview: e linear regression to find best a and b. • Then find α and β by applying α =1/b and β = a/b The mathematics of linear regression: Given : ( x1 , y1 ), ( x2 , y2 ), ( x3 , y3 )...( xn , yn ) To find : the straight line ( y ax b) that best fits the data n We must minimize E yi (axi b) 2 i 1 n a 2 xi b 2 yi 2abxi 2axi yi 2byi i 1 2 2 At the minimum : n E 2 2axi 2bxi 2 xi yi 0 a i 1 n E 2b 2axi 2 yi 0 b i 1 4 3/8/2010 Dividing both equations by 2 and expressing them in matrix form gives: xi 2 xi x a x y (1) b y i i i n where (1) (1) n i 1 i Solving using Cramer ’s Rule produces: a b1 b2 a12 a22 n xi yi xi yi 2 2 A n xi xi a11 b1 a21 b2 b A x y x x y n x x 2 i i 2 i i i 2 i i Aside: b is more easily calculated using b y ax Calculating of a and b involves first passing through the data points and calculating the following summations: y y x i i i 2 x y i i Once this is done formulas for a and b can be applied. For linear regression ONLY, the correlation coefficient r can be computed using: r n xi yi xi yi n xi xi 2 2 n yi yi 2 2 In addition to the summations listed above this requires y 2 i 5 3/8/2010 Linear Regression and the Casio Calculator: formula is y = Ax+B Mode Mode 2 (REG) REG stands for regression 1 (LIN) LIN stands for linear SHIFT CLR 1 (Scl) = clear statistical memory x1 , y1 DT the DT key is the M+ key x2 , y2 DT …. and so on until all points entered To retrieve value of A: SHIFT S‐VAR ‐> ‐> 1 (A) = the S‐VAR key is the 2 key, ‐> is right arrow To retrieve value of B: SHIFT S‐VAR ‐> ‐> 2 (B) = To retrieve the correlation coefficient: SHIFT S‐VAR ‐> ‐> 3 (r) = Other forms of regression are also supported. Polynomial regression: Linear regression involves fitting a first order polynomial (i.e. a polynomial of the form ax + b) to a set of data points. The basic idea is readily extended to higher order polynomials. Example: X: 0 Y: 189.4 3 95.1 6 34.1 9 1.8 12 7.3 15 46.7 18 131.9 21 253.2 We want to fit a quadratic (i.e. a polynomial of the form y = ax2+bx+c) to the data. This can be done by using polyfit and specifying a second order polynomial. >> polyfit (x, y, 2) % 2 for second order The result is a 3 element containing a, b, and c (in that order). 6 3/8/2010 >> xplot = linspace (‐1, 22, 100); >> yplot = polyval (p, xplot); >> plot (x, y, 'o', xplot, yplot, 'MarkerSize', 10); >> fprintf ('The best fit curve is %6.4f * x^2 + %6.4f * x + %6.4f\n',... p(1), p(2), p(3)); The best fit curve is 2.0088 * x^2 + ‐39.5105 * x + 193.4125 >> f = @(x) p(1) * x .^ 2 + p(2) * x + p(3); >> r = correlate (x, y, f); >> fprintf ('The correlation coefficient is %6.4f\n', r); The correlation coefficient is 0.9991 7 3/8/2010 The mathematics of quadratic regression: Given : ( x1 , y1 ), ( x2 , y2 ), ( x3 , y3 )...( xn , yn ) To find : the quadratic ( y ax 2 bx c) that best fits the data n We must minimize E yi (axi bxi c) i 1 At the minimum 2 E E E 0 a b c 2 First order equations Filling in the details gives : xi 4 3 xi x 2 i x x a x y x x b x y x (1) c y 3 i 2 i 2 i i 2 i i i i i n where (1) (1) n i 1 i The values of a, b, and c can be found by solving this series of equations. Equations = first order equations plus extra row and column. This pattern extends to higher order polynomials. 8...
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## This note was uploaded on 09/13/2013 for the course ECOR 2606 taught by Professor Goheen during the Fall '10 term at Carleton CA.

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