Dear Rui, thank you very much !
On Fri, May 24, 2019 at 4:35 AM Rui Barradas wrote:
> Hello,
>
> This has to do with what kind of variance estimator is being used.
> R uses the unbiased estimator and Python the MLE one.
>
>
>
> var1 <- function(x){
>n <- length(x)
>(sum(x^2) - sum(x)^2/n
Hello,
This has to do with what kind of variance estimator is being used.
R uses the unbiased estimator and Python the MLE one.
var1 <- function(x){
n <- length(x)
(sum(x^2) - sum(x)^2/n)/(n - 1)
}
var2 <- function(x){
n <- length(x)
(sum(x^2) - sum(x)^2/n)/n
}
sd1 <- function(x) sqrt
Dear all, please would you advise :
do python and R have different ways to compute the standard deviation (sd) ?
for example, in python, starting with :
a = np.array([[1,2,3], [4,5,6], [7,8,9]])
print(a.std(axis=1)) ### per row : [0.81649658 0.81649658 0.81649658]
print(a.std(axis=0)) ### per c
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