This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: figure of x2,y2 figure of x3,y3 figure of x4,y4 2.marginal distributions: I used such an Mfile: py=zeros(1,100); py1=zeros(100,1); [p,x,y,P]=pdf2d(x1,y1,100,100); for i=1:1:100 for j=1:1:100 py=py+p(j,:); end py(i)=py(i); end for j=1:1:100; for i=1:1:100; py1=py1+p(:,i); end py1(j)=py1(j); end subplot(211), plot(y,py); subplot(212), plot(y,(py1).'); subplot the resultes are shown as followings: the upper is the figure of X serious and the other is Y serious. px1(x) and py1(y) px2(x) and py2(y) px3(x) and py3(y) px4(x) and py4(y) 3. cov(x1,y1) ans = 0.4309 0.00030.0003 3.2938 cov(x2,y2) ans = 0.0835 0.00030.0003 0.0828 cov(x3,y3) ans = 2.4706 1.4910 1.4910 2.4953 cov(x4,y4) ans = 0.0829 0.0011 ρ (x1,y1)=0.0002, ρ (x2,y2)=0.0041, ρ (x3,y3)=0.6005, ρ (x4,y4)=0.0039 (b)Based on the correlation coefficient values, I think the sets of NO.1, NO.2 and NO.4 is uncorrelated. Because the coefficient values are extremely small. Using the pdm2d to do the analysis the joint probability density function of X and Y, I found that the NO1 and NO4 are independent....
View
Full Document
 Fall '04
 Karl
 Variance, Probability theory, probability density function, Peng Huang

Click to edit the document details