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Simple Error Bar
4 messages · mohan.radhakrishnan at polarisft.com, Jim Lemon, Rolf Turner
On 12/06/2013 04:16 PM, mohan.radhakrishnan at polarisft.com wrote:
Hi,
Basic question with basic code. I am simulating a set of
'y' values for a standard 'x' value measurement. So here the error bars
are very long because the
number of samples are very small. Is that correct ? I am plotting the mean
of 'y' on the 'y' axis.
Thanks,
Mohan
x<- data.frame(c(5,10,15,20,25,30,35,40,50,60))
colnames(x)<- c("x")
y<- sample(5:60,10,replace=T)
y1<- sample(5:60,10,replace=T)
y2<- sample(5:60,10,replace=T)
y3<- sample(5:60,10,replace=T)
y4<- sample(5:60,10,replace=T)
z<- data.frame(cbind(x,y,y1,y2,y3,y4))
z$mean<- apply(z[,c(2,3,4,5,6)],2,mean)
z$sd<- apply(z[,c(2,3,4,5,6)],2,sd)
z$se<- z$sd / sqrt(5)
Hi Mohan, As your samples seem to follow a discrete uniform distribution, the standard deviation is approximately the number of integers in the range (56) divided by the number of observations (10). Jim
Uh, no. You are forgetting to take the square root of 10, and to divide
by the square root of 12.
The variance of Y is (exactly) (56^2 - 1)/12, so the variance of Y-bar
is this quantity over 10,
so the standard deviation of Y-bar is sqrt((56^2 - 1)/12)/sqrt(10).
Which is approximately
(ignoring the -1) 56/sqrt(12) * 1/sqrt(10).
cheers,
Rolf
On 12/06/13 20:26, Jim Lemon wrote:
On 12/06/2013 04:16 PM, mohan.radhakrishnan at polarisft.com wrote:
Hi,
Basic question with basic code. I am simulating a
set of
'y' values for a standard 'x' value measurement. So here the error bars
are very long because the
number of samples are very small. Is that correct ? I am plotting the
mean
of 'y' on the 'y' axis.
Thanks,
Mohan
x<- data.frame(c(5,10,15,20,25,30,35,40,50,60))
colnames(x)<- c("x")
y<- sample(5:60,10,replace=T)
y1<- sample(5:60,10,replace=T)
y2<- sample(5:60,10,replace=T)
y3<- sample(5:60,10,replace=T)
y4<- sample(5:60,10,replace=T)
z<- data.frame(cbind(x,y,y1,y2,y3,y4))
z$mean<- apply(z[,c(2,3,4,5,6)],2,mean)
z$sd<- apply(z[,c(2,3,4,5,6)],2,sd)
z$se<- z$sd / sqrt(5)
Hi Mohan, As your samples seem to follow a discrete uniform distribution, the standard deviation is approximately the number of integers in the range (56) divided by the number of observations (10).
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