ez.se: standard error of mean

In jerryzhujian9/zmisc: ez Zhu's miscellaneous functions

standard error of mean

Description

standard error of mean

Usage

ez.se(x)

ez.sd(x, na.rm = FALSE)

Arguments

x

a vector

Note

na will be omitted before calculation, the formula is sqrt(var(x,na.rm=TRUE)/length(na.omit(x))) (equivalent to sd(x,na.rm=TRUE)/sqrt(length(na.omit(x))))

sd, standard deviation (sigma or sd, s) is simply the (positive) square root of the variance (sigma^2, or s^2), var. Both sd(), var() use denominator n - 1, which gives an unbiased estimator of the (co)variance for i.i.d. observations.

se = sd/sqrt(n). see https://www.statsdirect.com/help/basic_descriptive_statistics/standard_deviation.htm

I wrote ez.se, ez.sd = sd


For zscore, (x-mean(x,na.rm=T))/sd(x,na.rm=T), or use ez.scale(x,center=TRUE,scale=TRUE) demean: ez.scale(x,center=TRUE,scale=FALSE). (ez.scale() auto NA ignored/returned in place. )
z-scores indeed have a mean of zero and a standard deviation of 1. Other than that, however, z-scores follow the exact same distribution as original scores. That is, standardizing scores doesn't make their distribution more or less "normal" in any way. see https://www.spss-tutorials.com/z-scores-what-and-why/

jerryzhujian9/zmisc documentation built on March 9, 2024, 12:49 a.m.