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...th > > var(x) > > > but there is no covariance, the joint variance of two rv's. "co" or > joint with what if you have only x? > Note that the variance of x[1] or any other vector element is zero, > it's only one value therefore it does not vary. A similar reasonong > can be applied to cov(x[1], x[2]), etc. > > Hope this helps, > > Rui Barradas > > ?s 12:14 de 04/10/2024, Steven Yen escreveu: >> Hello >> >> I have a vector: >> >> set.seed(123) > n<-3 > x<-rnorm(n); x [1] -0.56047565 -0.23017749 &...

...tor you can estimate its variance with var(x) but there is no covariance, the joint variance of two rv's. "co" or joint with what if you have only x? Note that the variance of x[1] or any other vector element is zero, it's only one value therefore it does not vary. A similar reasonong can be applied to cov(x[1], x[2]), etc. Hope this helps, Rui Barradas ?s 12:14 de 04/10/2024, Steven Yen escreveu: > Hello > > I have a vector: > > set.seed(123) > n<-3 > x<-rnorm(n); x [1] -0.56047565 -0.23017749 > 1.55870831 I like to create a matrix with elem...

...> >> >> but there is no covariance, the joint variance of two rv's. "co" or >> joint with what if you have only x? >> Note that the variance of x[1] or any other vector element is zero, >> it's only one value therefore it does not vary. A similar reasonong >> can be applied to cov(x[1], x[2]), etc. >> >> Hope this helps, >> >> Rui Barradas >> >> ?s 12:14 de 04/10/2024, Steven Yen escreveu: >>> Hello >>> >>> I have a vector: >>> >>> set.seed(123) > n<-3 >...

Hello I have a vector: set.seed(123) > n<-3 > x<-rnorm(n); x [1] -0.56047565 -0.23017749 1.55870831 I like to create a matrix with elements containing variances and covariances of x. That is var(x[1]) cov(x[1],x[2]) cov(x[1],x[3]) cov(x[2],x[1]) var(x[2]) cov(x[2],x[3]) cov(x[3],x[1]) cov(x[3],x[2]) var(x[3]) And I like to do it with "apply". Thanks. On 10/4/2024 6:35