Solutions HW 7 - 13.7 The function can be linearized by...

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Solutions HW#7 12.7 The equations to be solved are The partial derivatives can be computed and evaluated at the initial guesses They can then be used to compute the determinant of the Jacobian for the first iteration is -1.4(-7)-(-1)(-3.6)=6.2 The values of the functions can be evaluated at the initial guesses as These values can be substituted into Eq. (12.12) to give The computation can be repeated until an acceptable accuracy is obtained. The results are summarized as
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13.5 The results can be summarized as We can also plot both lines on the same graph Thus, the “best” fit lines and the standard errors differ. This makes sense because different errors are being minimized depending on our choice of the dependent (ordinate) and independent (abscissa) variables. In contrast, the correlation coefficients are identical since the same amount of uncertainty is explained regardless of how the points are plotted.
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Unformatted text preview: 13.7 The function can be linearized by dividing it by x and taking the natural logarithm to yield Therefore, if the model holds, a plot of ln( y / x ) versus x should yield a straight line with an intercept of ln 4 and a slope of 4 . Plot of linearized data with linear regression fit: This equation can be plotted together with the data: 4 9.661786 = , 4-2.4733 = 13.11 The power fit can be determined as Plot of linearized data with linear regression fit: Here is a plot of the fit along with the original data: The value of the surface area for a 95-kg person can be estimated as Surface area for a 95 kg person 2.34 2 m 13.18 A log-log plot of versus T suggests a linear relationship The model and the data can be plotted on untransformed scales as 2 38,147.94 = , 2 3.01338 = -...
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This note was uploaded on 09/05/2011 for the course EGM 3344 taught by Professor Raphaelhaftka during the Fall '09 term at University of Florida.

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Solutions HW 7 - 13.7 The function can be linearized by...

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