# Matlab2 - Part I x =[1928 1932 1936 1948 1952 1956 1964...

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September 22, 2015 Part I x = [1928 1932 1936 1948 1952 1956 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004]; y = [12.2 11.9 11.5 11.9 11.5 11.5 11.4 11.08 11.07 11.08 11.06 10.97 10.54 10.82 10.94 10.75 10.93]; plot(x,y) xlabel( 'Year' ) ylabel( 'Finishing Time(sec)' ) title( 'Curve Fitting Exercise' ) Fit1=polyfit(x,y,1); y1=polyval(Fit1,x) RMS=(mean((y-y1).^2))^0.5 hold on ; plot(x,y1, 'r' ) plot(1960,11.0, 'gd' ) legend( 'original Data' , 'Fit1' , 'plot' ) B. Fit1=-0.0185, 47.6837 C. RMS=0.1691 E. The line does not accurately reflect that value. The best fit line is about 0.4 about the point on the y- axis. The error is about double the RMS error F. I predict that the finish time will be about 10.7 seconds. My prediction based off of the best fit line is about 0.23 seconds off from the actual time. It’s close to the RMS error.

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Unformatted text preview: Part II hold on ; plot(xvals,yvals); xlabel( 'xvals' ) ylabel( 'yvals' ) title( 'Curve Fitting Exercise Part II' ) Fit1=polyfit(xvals,yvals,1); y1=polyval(Fit1,xvals); hold on ; plot(xvals,y1, 'g--' ) RMS=(mean((yvals-y1).^2))^0.5; %RMS=13.7048 %Fit1=[11.0086,1.2999] Fit2=polyfit(xvals,yvals,2); y2=polyval(Fit2,xvals); plot(xvals,y2, 'b.' ); RMS2=(mean((yvals-y2).^2))^0.5; %RMS2=13.6202 %Fit2=[-0.1999,11.0086,2.9999] Fit3=polyfit(xvals,yvals,3); y3=polyval(Fit3,xvals); plot(xvals,y3, 'rd' ); RMS3=(mean((yvals-y3).^2))^0.5; %RMS3=2.0896-14 %Fit2=[0.6999,-0.1999,0.3000,2.9999 legend( 'original Data' , 'Fit1' , 'Fit2' , 'Fit3' ) RMS=13.7048 Fit1=[11.0086,1.2999] RMS2=13.6202 Fit2=[-0.1999,11.0086,2.9999] RMS3=2.0896-14 Fit2=[0.6999,-0.1999,0.3000,2.9999...
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