Question: Explanation and complete code gets thumbs up. For this Matlab problem. For Problems 1-3, use Matlab to plot the data and the best fit. Show

Explanation and complete code gets thumbs up. For this Matlab problem.

For Problems 1-3, use Matlab to plot the data and the best fit. Show the written solutions to all the problems, and the Matlab graphs.

Explanation and complete code gets thumbs up. For this Matlab problem. For

. Problem 1. Use least squares regression to fit a straight line to 0 2 4 6 9 11 12 15 17 19 y5 676 9871012 12 Along with the slope and intercept, compute the correlation coefficient. Plot the data and the regression line. Then repeat the problem, but regress z versus y that is, switch the variables. . Problem 2. Fit the following data with the power model y - arb. Use the resulting power equation to predict y at x = 9. 2.5 3.5 5 6 7.5 10 12.5 15 17.5 20 y13118.58.276.25.24.8 4.64.3 . Problem 3. Consider the model y = a4ae34. Linearize this model and use it to estimate 04 and B4 based on the following data. 0.0.20.4 0.6 0.913 1.5 1.71.8 0.751.251.45 1.250.850.550.350.28 0.18 . Problem 4. Consider the following model y = aix+a2x2+e. Determine the matrix [M] and the vector [B) for the least squares fitting of data points (n) using this model Use two different methods: 1. Minimization of the sum of the squares of the residuals 2. Generalized least squares approach . Problem 1. Use least squares regression to fit a straight line to 0 2 4 6 9 11 12 15 17 19 y5 676 9871012 12 Along with the slope and intercept, compute the correlation coefficient. Plot the data and the regression line. Then repeat the problem, but regress z versus y that is, switch the variables. . Problem 2. Fit the following data with the power model y - arb. Use the resulting power equation to predict y at x = 9. 2.5 3.5 5 6 7.5 10 12.5 15 17.5 20 y13118.58.276.25.24.8 4.64.3 . Problem 3. Consider the model y = a4ae34. Linearize this model and use it to estimate 04 and B4 based on the following data. 0.0.20.4 0.6 0.913 1.5 1.71.8 0.751.251.45 1.250.850.550.350.28 0.18 . Problem 4. Consider the following model y = aix+a2x2+e. Determine the matrix [M] and the vector [B) for the least squares fitting of data points (n) using this model Use two different methods: 1. Minimization of the sum of the squares of the residuals 2. Generalized least squares approach

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