TEST3_LO - constants of the original non-linear equation....

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Learning Objectives for TEST 3, 2009 *Be able to write MATLAB programs for any numerical method covered Ch 8. Solving Systems of Equations Solve a system of nonlinear equations using multidimensional Newton Raphson Determine the speed of convergence of multidimensional Newton Raphson Ch 9. Least Squares Curve Fitting Describe the purpose and use of curve fitting Explain the advantage of using a “least squares” approach to curve fitting versus Minimizing the sum of the error Minimizing the sum of the absolute values of the error Minimizing the maximum error Derive and explain equations for “fitting constants” for least-squares regression by minimizing sum of squared residuals Perform linear regression or polynomial regression and calculate the Fitting constants Correlation coefficient Standard error of the estimate Linearize several “non-linear” functions and then use “linear regression” to find
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Unformatted text preview: constants of the original non-linear equation. Describe how true nonlinear regression works Ch 10. Interpolation Describe the Basic Idea and motivation Compare and contrast with Curve Fitting Explain situations where interpolation would be more useful than curve fitting and vice versa Use Newton Divided Difference and describe how it is derived Use Lagrange Interpolation and compare/constrast to Newton Divided Difference Describe the advantages/disadvantages of use single interpolating polynomials of arbitrary degree versus using piecewise polynomials Describe how linear and cubic spline interpolation is derived and its key properties Use linear and/or cubic spline interpolation for a given data set to create an interpolating equation and evaluate the function at an arbitrary point...
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This note was uploaded on 04/21/2010 for the course PGE 310 taught by Professor Klaus during the Spring '06 term at University of Texas at Austin.

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