基于MATLAB的数据处理与统计作图.docx
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基于MATLAB的数据处理与统计作图.docx
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基于MATLAB的数据处理与统计作图
Smooth函数:
loadcount.dat;
c=smooth(count(:
));
C1=reshape(c,24,3);
subplot(3,1,1);plot(count,':
');
holdon;
plot(C1,'-');
C2=zeros(24,3);
forI=1:
3
C2(:
I)=smooth(count(:
I));
end;
subplot(3,1,2);plot(count,':
');holdon;
plot(C2,'-');
subplot(3,1,3);plot(C2-C1,'o-');
>>x=15*rand(150,1);
y=sin(x)+0.5*(rand(size(x))-0.5);
y(ceil(length(x)*rand(2,1)))=3;
noise=normrnd(0,15,150,1);
y=y+noise;
>>yy1=smooth(x,y,0.1,'loess');
>>yy2=smooth(x,y,0.1,'rloess');
>>yy3=smooth(x,y,0.1,'moving');
>>yy4=smooth(x,y,0.1,'lowess');
>>yy5=smooth(x,y,0.1,'sgolay');
>>yy6=smooth(x,y,0.1,'rlowess');
>>[xx,ind]=sort(x);
subplot(3,2,1);plot(xx,y(ind),'b-.',xx,yy1(ind),'r-');
subplot(3,2,2);plot(xx,y(ind),'b-.',xx,yy2(ind),'r-');
subplot(3,2,3);plot(xx,y(ind),'b-.',xx,yy3(ind),'r-');
subplot(3,2,4);plot(xx,y(ind),'b-.',xx,yy4(ind),'r-');
subplot(3,2,5);plot(xx,y(ind),'b-.',xx,yy5(ind),'r-');
subplot(3,2,6);plot(xx,y(ind),'b-.',xx,yy6(ind),'r-');
Smoothts函数:
>>x=122+rand(500,4);
p=x(:
4)';
out1=smoothts(p,'b',30);
out2=smoothts(p,'b',100);
out3=smoothts(p,'g',30);
out4=smoothts(p,'g',100,100);
out5=smoothts(p,'e',30);
out6=smoothts(p,'e',100);
subplot(2,2,1);plot(p);
subplot(2,2,2);plot(out1,'k');holdon;plot(out2,'m.');
subplot(2,2,3);plot(out3,'k');holdon;plot(out4,'m.');
subplot(2,2,4);plot(out5,'k');holdon;plot(out6,'m.');
Medfilt1函数:
x=linspace(0,2*pi,250)';
y=sin(x)*150;
y(ceil(length(x)*rand(2,1)))=3;
noise=normrnd(0,15,250,1);
y=y+noise;
subplot(1,2,1);plot(x,y);
yy=medfilt1(y,50);
subplot(1,2,2);plot(x,y,'r-.');
holdon;plot(x,yy,'.');
直方图:
Hist函数:
x=randn(499,1);
y=randn(499,3);
subplot(3,1,1);hist(x);
subplot(3,1,2);hist(x,100);
subplot(3,1,3);hist(y,25);
Histc函数:
x=-3.9:
0.1:
3.9;
y=randn(10000,1);
subplot(1,2,1);hist(y,x);
n=histc(y,x);
c=cumsum(n);
subplot(1,2,2);bar(x,c);
Histfit函数:
>>r=normrnd(10,1,10,1);
>>histfit(r);
>>h=get(gca,'Children');
盒子图:
>>N=1024;x1=normrnd(5,1,N,1);
>>x2=normrnd(6,1,N,1);x=[x1x2];subplot(2,2,1);sym1='*';notch1=1;boxplot(x,notch1,sym1);
>>subplot(2,2,2);notch2=0;boxplot(x,notch2);
x=randn(100,25);subplot(2,1,1);boxplot(x);
误差条图:
>>x=0:
pi/10:
pi;
y=sin(x);
e=std(y)*ones(size(x));
errorbar(x,y,e)
最小二乘法拟合直线:
>>x=1:
10;
>>y1=x+rand(1,10);
>>scatter(x,y1,25,'b','*')
>>holdon;
>>y2=2*x+randn(1,10);
>>plot(x,y2,'mo')
>>y3=3*x+randn(1,10);
>>plot(x,y3,'rx:
');
>>y4=4*x+randn(1,10);
>>plot(x,y4,'g+--');
帕累托图:
>>codelines=[200120555608102410157687];
>>coders={'Fred','Ginger','Norman','Max','Julia','Wally','Heidi','Pat'};
>>pareto(codelines,coders)
QQ图:
>>M=100;N=1;
>>x=normrnd(0,1,M,N);
>>y=rand(M,N);
>>z=[x,y];
>>subplot(2,2,1);h1=qqplot(z);
>>gridon;
>>x=normrnd(0,1,100,1);
>>y=normrnd(0.5,2,50,1);
>>subplot(2,2,2);
>>h2=qqplot(x,y);
>>gridon;
>>x=normrnd(5,1,100,1);
>>y=weibrnd(2,0.5,100,1);
>>subplot(2,2,3);
>>h3=qqplot(x,y);
>>gridon;
>>subplot(2,2,4);
>>x=normrnd(10,1,100,1);
>>subplot(2,2,4);
>>qqplot(x);
回归残差图:
>>X=[ones(10,1)(1:
10)'];
>>y=X*[10;1]+normrnd(0,0.1,10,1);
>>[b,bint,r,rint]=regress(y,X,0.06);
>>rcoplot(r,rint);
多项式拟合曲线:
>>p=[1-2-10];
>>t=0:
0.1:
3;
>>y=polyval(p,t)+0.5*randn(size(t));
>>plot(t,y,'ro');
>>h=refcurve(p);
>>set(h,'Color','r');
>>q=polyfit(t,y,3);
>>refcurve(q);
参考线:
>x=1:
10;
>>y=x+randn(1,10);
>>scatter(x,y,25,'b','*');
>>lsline;
Noallowedlinetypesfound.Nothingdone.
>>mu=mean(y);
>>hline=refline([0mu]);
>>set(hline,'Color','r');
正态概率图:
>>M=100;N=1;
>>x=normrnd(0,1,M,N);
>>y=rand(M,N);
>>z=[x,y];
>>h=normplot(z);
>>gridon;
点的标签:
>>loadcities;
>>education=ratings(:
6);
>>arts=ratings(:
7);
>>plot(education,arts,'+');
>>gname(names)
工序能力指数:
>>data=normrnd(1,1,30,1);
>>[p,cp,cpk]=capable(data,[-3,3])
p=
0.0245
cp=
1.0284
cpk=
0.6562
规定区间的正态分布密度图:
>>p=normspec([10Inf],11.5,1.25);
>>gridon;
标准差管理图:
>>loadparts;
>>schart(runout);
>>gridon;
均值管理图:
>>loadparts;
>>xbarplot(runout);gridon;
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