dimensional data string

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marciuc
marciuc 2011-3-31
I have an exercise in mathlab .....
load xx18.dat
load yy18.dat
plot(xx18,yy18)
a=mean(xx18)
b=mean(yy18)
c=mean(xx18.^2)
d=mean(yy18.^2)
e=mean(xx18.*yy18)
r=(e-a*b)/sqrt((c-a^2)*(d-b^2))
p=polyfit(xx18,yy18,2)
yt=polyval(p,xx18)
subplot(2,3,1)
plot(xx18,yy18,'r', xx18, yt)
title('Y(X) si Yteoretic(X)')
eroare= yy18-yt
subplot(2,3,2)
plot( eroare,xx18)
title('graficul erorii in raport cu X')
subplot(2,3,3)
plot(eroare,yt)
title('graficul erorii in raport cu Yteoretic')
subplot
load xx18.dat
load yy18.dat
plot(xx18,yy18)
a=mean(xx18)
b=mean(yy18)
c=mean(xx18.^2)
d=mean(yy18.^2)
e=mean(xx18.*yy18)
r=(e-a*b)/sqrt((c-a^2)*(d-b^2))
p=polyfit(xx18,yy18,2)
yt=polyval(p,xx18)
subplot(2,3,1)
plot(xx18,yy18,'r', xx18, yt)
title('Y(X) si Yteoretic(X)')
eroare= yy18-yt
subplot(2,3,2) plot( eroare,xx18) title('graficul erorii in raport cu X')
subplot(2,3,3)
plot(eroare,yt)
title('graficul erorii in raport cu Yteoretic')
subplot(2,3,4)
for i=2:length(eroare) hold on plot(eroare(i-1), eroare(i), '*') end
title('graficul punctelor succesive')
subplot(2,3,5)
hist(eroare,10)
title('histograma erorilor')
subplot(2,3,6)
normplot(eroare)
title('graficul probabilitatilor normale')
v=1:length(eroare)
m=polyfit(v,eroare,1)
How do I calculate the slope, where slope is 0 and the location is constant?
and i don't understand those graphs that are generated... 6 graphics ...and what involving them?
  4 个评论
显示 2更早的评论
Paulo Silva
Paulo Silva 2011-3-31
I had some teachers like those in my time, good luck
Jan
Jan 2011-3-31
I assume the titles over the diagrams contain some helpful hints... Sometimes asking even not normal persons yields to useful answers. It is always worth to try it.

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回答（1 个）

Matt Tearle
Matt Tearle 2011-3-31
polyfit is finding the least-squares best fit to the data for a quadratic polynomial. polyval then evaluates this polynomial at the x data values. Hence yt is a vector of the y values for the fitted (theoretical) model. eroare is therefore the vector of residuals -- ie the difference between the y values predicted by the fitted model and the actual data.
The graphs are showing the data and the fitted model, then various investigations of the residuals, specifically independence and normality.

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