[Solved]1 Consider Experiment X Variable Control Y Measured Variable Desired Fit Nonlinear Equatio Q37274393

Can you write in Matlab ? I need implementations.

1 Consider an experiment in which x is the variable that you can control and y is the measured variable. It is desired to fit the nonlinear equation yto the data given below: 0.2 1.25 0.4 1.45 0.6 1.25 0.85 0.9 0.55 0.35 0.280.18 0.75 a) Plot y versus x as data points. Set the axes limits to be xmin-0, xma,-2, ymin-0, ymax-1.8 Note: In your Matlab program, you are allowed to use the built-in functions sum and mean b) Linearise the given nonlinear equation and then apply linear least-squares regression to find the equation for the regression line. Plot the linearised data set together with the regression line. Determine Sr (the sum of squares of residuals around the regression line) and r(the corresponding coefficient of determination). c) Using the results of part (b), determine the coefficients α, β of the nonlinear equation. Plot the original data set together with the nonlinear equation. Determine S (the sum of squares of residuals around the nonlinear equation fit) and (the corresponding coefficient of determination). In your plot, use the same axes limits given in part (a). d) What can you say about the goodness of the curve-fits applied in parts (b) and (c). Comment shortly 2) Bonus (+10 pts): Using Matlab’s built-in functions polyfit and polyval, fi a 4h order polynomial to the given data (i.e. apply polynomial regression) and plot the data set together with the fitted polynomial. 3) Bonus (+15 pts): Part (b) of Question-1 can be solved very easily and quickly using Matlab’s “Basic Fitting” menu. Plot the linearised data set together with the regression line, find S, and check your answers (Note that the norm of residuals is equal to-/Sr ) . Do not calculate 4) Bonus (+10 pts): Using Matlab’s “Basic Fitting” menu, fit cubic, 4th order and Sth order polynomials to the given data. You are only required to plot the data set together with the fitted polynomials and indicate the most successful curve-fit. Show transcribed image text 1 Consider an experiment in which x is the variable that you can control and y is the measured variable. It is desired to fit the nonlinear equation yto the data given below: 0.2 1.25 0.4 1.45 0.6 1.25 0.85 0.9 0.55 0.35 0.280.18 0.75 a) Plot y versus x as data points. Set the axes limits to be xmin-0, xma,-2, ymin-0, ymax-1.8 Note: In your Matlab program, you are allowed to use the built-in functions sum and mean b) Linearise the given nonlinear equation and then apply linear least-squares regression to find the equation for the regression line. Plot the linearised data set together with the regression line. Determine Sr (the sum of squares of residuals around the regression line) and r(the corresponding coefficient of determination). c) Using the results of part (b), determine the coefficients α, β of the nonlinear equation. Plot the original data set together with the nonlinear equation. Determine S (the sum of squares of residuals around the nonlinear equation fit) and (the corresponding coefficient of determination). In your plot, use the same axes limits given in part (a). d) What can you say about the goodness of the curve-fits applied in parts (b) and (c). Comment shortly 2) Bonus (+10 pts): Using Matlab’s built-in functions polyfit and polyval, fi a 4h order polynomial to the given data (i.e. apply polynomial regression) and plot the data set together with the fitted polynomial. 3) Bonus (+15 pts): Part (b) of Question-1 can be solved very easily and quickly using Matlab’s “Basic Fitting” menu. Plot the linearised data set together with the regression line, find S, and check your answers (Note that the norm of residuals is equal to-/Sr ) . Do not calculate 4) Bonus (+10 pts): Using Matlab’s “Basic Fitting” menu, fit cubic, 4th order and Sth order polynomials to the given data. You are only required to plot the data set together with the fitted polynomials and indicate the most successful curve-fit.

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Answer to 1 Consider an experiment in which x is the variable that you can control and y is the measured variable. It is desired t… . . .