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15.6 LAB: Has the curve flattened? 401996 26016540x3zy? LAB ACTIVITY 15.6.1: LAB: Has the curve flattened? Has the curve flattened? During the Coronavirus pandemic, statistics such as number of new daily cases are gathered to help study the spread of the virus. A country's cumulative new case data after 100 cases is provided for this problem. The da https://informationisbeautiful.net/data/ (https://informationisbeautiful.net/data/) and shows accumulative data for shown in the figure below. 1.5 *105 For Y(x): 11 0.5- 0 0 20 0/ This tool is provided by a third party. Though your activity may be recorded, a page refresh may be needed to fill the banner. 1 € = Accumulative Case Number () 40 60 Days One important statistical parameter is the maximum value. Finding the maximum value and the relative location like the date of the occuracne of the maximum number of cases, can help in further studies of the progression 80 From the above, data can be modeled with an exponential function in the form of Y(x) = C*(1-exp(-X/D)), where parameters of the model. However an exponential model can be hard to work with. An exponential model cant Taylor expansion (https://en.wikipedia.org/wiki/Taylor_se ... l_function). In other words: 1+x+ + + ... + 21 31 100 Y(x) = C*(1-e 120 140 - e ²5) = 0 C 3! Write a program that finds the linearization parameters for this data set. Between orders 2 to 5, which polynom +...]
the data? The dataset is accumultive data, so no negative data points exist. However, some polynomial model: data points due to modeling error. The nonphysical data points don't have any significant effect in the modeling problem. The best polynomial fit order can be decided visually by plotting the original data and the linearized model, and Given: ▪ accumNum: a 1D array containing accumulative number of cases for 128 days. The output is: ▪ polyNomOrder: the order of polynomial best fitting data. • polyNomCoeff: the coefficients of the polynomial equation. Restrictions: polyfit() cannot be used for this problem, as this system is an overdetermined system of linear eq coefficients should be found solving the system of linear euqations. Script> 1 accumNum = importdata('cData.txt'); 2 X= [0: length(accumNum)-1]'; 3 Save 7 title('Infection'); 8 hold on; 4 % To recreate the plot in the description of the problem. 5 figure (1); 6 plot( X, accumNum, 'r.' ); C Reset 12 polyNomOrder = interp1(x, accumNum, 5) 13 9 10 % polyNomOrder is the plolynomial order that best fits the data. 11 % Visually check to guess an order between 2-5 MATLAB Documentation (https 14 Write the coefficent model Xc*A = AccumNum 15 Xc = zeros(length(accumNum), polyNomOrder); 16 end 17% Use for loops or another means to calculate Xc = [X(i), X(i)^2, X(i)^3 ...]; 18 for i=1: length (accumNum); 19 for j=1: polyNomOrder+1; 20 Xc(i,j)= X(i)^(j-1); 21 22 end 23 24 % Write the coefficent model Xc PolyNomCoeff = AccumNum 25 %% XE = [X(i), X(i)^2, X(i)^3 ...]; 26 polyNomCoeff = zeros (length(accumNum), Xc); 27 28 % Shows the data and the model in one figure and can be compared. 29 % How closely does the model match the data? Experiment with changing PolynomC 30 plot( X, Xc*polyNomCoeff, 'b-' );
31 hold off; Assessment: 0 of 3 Tests Passed Is the order of the polynomial determined correctly? Error using zeros Size inputs must be scalar. Error in solution (line 26) polyNomCoeff = zeros(length(accumNum), Xc); Are the coefficients calculated correctly? Error using zeros Size inputs must be scalar. High orders should model the data more closely. Error in solution (line 26) polyNomCoeff = zeros(length(accumNum), Xc); Error using zeros Size inputs must be scalar. Output polyfit() should not be used. Error in solution (line 26) polyNomCoeff = zeros(length(accumNum), Xc); 639 polyNomOrder = Error using zeros: Size inputs must be scalar. Error in solution (line 26) polyNomCoeff zeros (length (accumNum), Xc); 2-105 Infection