1. The Islamic University of Gaza Faculty of Engineering Civil Engineering Department Numerical Analysis ECIV 3306 Chapter 17 Least Square Regression

2. CURVE FITTING Part 5 Describes techniques to fit curves (curve fitting) to discrete data to obtain intermediate estimates. There are two general approaches for curve fitting: Least Squares regression: Data exhibit a significant degree of scatter. The strategy is to derive a single curve that represents the general trend of the data. Interpolation: Data is very precise. The strategy is to pass a curve or a series of curves through each of the points.

3. Introduction In engineering, two types of applications are encountered: – Trend analysis. Predicting values of dependent variable, may include extrapolation beyond data points or interpolation between data points. – Hypothesis testing. Comparing existing mathematical model with measured data.

5. Mathematical Background Arithmetic mean. The sum of the individual data points (yi) divided by the number of points (n). Standard deviation. The most common measure of a spread for a sample.

6. Mathematical Background (cont’d) Variance. Representation of spread by the square of the standard deviation. or Coefficient of variation. Has the utility to quantify the spread of data.

7. Least Squares Regression Chapter 17 Linear Regression Fitting a straight line to a set of paired observations: (x 1, y 1 ), (x 2, y 2 ),…,(x n, y n ). y = a 0 + a 1 x + e a 1 - slope a 0 - intercept e - error, or residual, between the model and the observations

8. Linear Regression: Residual

9. Linear Regression: Question How to find a 0 and a 1 so that the error would be minimum?

10. Linear Regression: Criteria for a “Best” Fit e1e1 e2e2 e 1 = -e 2

11. Linear Regression: Criteria for a “Best” Fit

13. Linear Regression: Least Squares Fit Yields a unique line for a given set of data.

14. Linear Regression: Least Squares Fit The coefficients a 0 and a 1 that minimize S r must satisfy the following conditions:

15. Linear Regression: Determination of a o and a 1 2 equations with 2 unknowns, can be solved simultaneously

16. Linear Regression: Determination of ao and a1

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20. Error Quantification of Linear Regression Total sum of the squares around the mean for the dependent variable, y, is S t Sum of the squares of residuals around the regression line is S r

21. Error Quantification of Linear Regression S t -S r quantifies the improvement or error reduction due to describing data in terms of a straight line rather than as an average value. r 2: coefficient of determination r : correlation coefficient

22. Error Quantification of Linear Regression For a perfect fit: S r = 0 and r = r 2 =1, signifying that the line explains 100 percent of the variability of the data. For r = r 2 = 0, S r = S t, the fit represents no improvement.

23. Least Squares Fit of a Straight Line: Example Fit a straight line to the x and y values in the following Table: xixi yiyi xiyixiyi xi2xi2 10.5 1 22.554 3269 4416 53.517.525 6636 75.538.549 2824119.5140

24. Least Squares Fit of a Straight Line: Example (cont’d) Y = 0.07142857 + 0.8392857 x

25. Least Squares Fit of a Straight Line: Example (Error Analysis) x i y i 1 0.5 2 2.5 3 2.0 4 4.0 5 3.5 6 6.0 7 5.5 8.5765 0.1687 0.8622 0.5625 2.0408 0.3473 0.3265 0.3265 0.0051 0.5896 6.6122 0.7972 4.2908 0.1993 28 24.0 22.7143 2.9911

26. Least Squares Fit of a Straight Line: Example (Error Analysis) The standard deviation (quantifies the spread around the mean): The standard error of estimate (quantifies the spread around the regression line) Because, the linear regression model has good fitness

27. Algorithm for linear regression

28. Linearization of Nonlinear Relationships The relationship between the dependent and independent variables is linear. However, a few types of nonlinear functions can be transformed into linear regression problems.  The exponential equation.  The power equation.  The saturation-growth-rate equation.

30. Linearization of Nonlinear Relationships 1. The exponential equation. y* = a o + a 1 x

31. Linearization of Nonlinear Relationships 2. The power equation y* = a o + a 1 x*

32. Linearization of Nonlinear Relationships 3. The saturation-growth-rate equation y* = 1/y a o = 1/a 3 a 1 = b 3 /a 3 x* = 1/x

33. Example Fit the following Equation: to the data in the following table: x i y i 1 0.5 2 1.7 3 3.4 4 5.7 5 8.4 15 19.7 X*=log x i Y*=logy i 0 -0.301 0.301 0.226 0.477 0.534 0.602 0.753 0.699 0.922 2.079 2.141

34. Example XiYiX* i =Log(X)Y* i =Log(Y)X*Y*X*^2 10.50.0000-0.30100.0000 21.70.30100.23040.06940.0906 33.40.47710.53150.25360.2276 45.70.60210.75590.45510.3625 58.40.69900.92430.64600.4886 Sum1519.7002.0792.1411.4241.169

35. Linearization of Nonlinear Functions: Example log y=-0.334+1.75log x

36. Polynomial Regression Some engineering data is poorly represented by a straight line. For these cases a curve is better suited to fit the data. The least squares method can readily be extended to fit the data to higher order polynomials.

37. Polynomial Regression (cont’d) A parabola is preferable

38. Polynomial Regression (cont’d) A 2 nd order polynomial (quadratic) is defined by: The residuals between the model and the data: The sum of squares of the residual:

39. Polynomial Regression (cont’d) 3 linear equations with 3 unknowns (a o,a 1,a 2 ), can be solved

40. Polynomial Regression (cont’d) A system of 3x3 equations needs to be solved to determine the coefficients of the polynomial. The standard error & the coefficient of determination

41. Polynomial Regression (cont’d) General: The mth-order polynomial: A system of (m+1)x(m+1) linear equations must be solved for determining the coefficients of the mth-order polynomial. The standard error: The coefficient of determination:

42. Polynomial Regression- Example Fit a second order polynomial to data: xixi yiyi xi2xi2 xi3xi3 xi4xi4 xiyixiyi xi2yixi2yi 02.100000 17.7111 213.6481627.254.4 327.29278181.6244.8 440.91664256163.6654.4 561.125125625305.51527.5 15152.655225979585.62489

43. Polynomial Regression- Example (cont’d) The system of simultaneous linear equations:

44. Polynomial Regression- Example (cont’d) xixi yiyi y model ei2ei2 (y i -y`) 2 02.12.47860.14332 544.42889 17.76.69861.00286 314.45929 213.614.641.08158 140.01989 327.226.3030.80491 3.12229 440.941.6870.61951 239.22809 561.160.7930.09439 1272.13489 15152.63.74657 2513.39333 The standard error of estimate: The coefficient of determination: