linear_regression

# linear_regression - ao=ysum-a1*xsum for i=1:n yi=y(i xi=x(i...

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function linear_regression(x,y) %Problem 13.5 %generates a graph of experimental data with a 'best fit' line n=numel(x); maxs=max(x); xold=0; yold=0; xi2old=0; yixi_old=0; srold=0; St_old=0; ysum=sum(y)/n; xsum=sum(x)/n; for i=1:n xnew=x(i); xi=xnew+xold; xold=xi; ynew=y(i); yi=ynew+yold; yold=yi; yixi=x(i)*y(i); yixi=yixi+yixi_old; yixi_old=yixi; xi2=x(i)^2; xi2=xi2+xi2old; xi2old=xi2; St=(ysum-y(i))^2+St_old; St_old=St; end a1=(n*yixi-xi*yi)/(n*xi2-xi^2);

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Unformatted text preview: ao=ysum-a1*xsum; for i=1:n yi=y(i); xi=x(i); Sr=(yi-ao-a1*xi)^2; Sr=Sr+srold; srold=Sr; end x3=[0:.1:maxs]'; y2=ao+a1*x3; %Standard Error of the estimate sxy=sqrt(Sr/(n-2)) %correlation coefficient r=sqrt((St-Sr)/St) fprintf('y=%7.6f+%7.6fx\n\n',ao,a1); AXIS=([0 20 0 12]); plot(x,y,'s',x3,y2) hold on %--------------------------------------------------------------------------...
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linear_regression - ao=ysum-a1*xsum for i=1:n yi=y(i xi=x(i...

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