Z-score can be calculated with below formula,
z = ( x - μ ) / σ
where,
x - x vector (elements of x vector)
μ - mean value of x vector
σ - standard deviation of x vector
The normal distribution curve can easily explain a z-score. Z-score values are located around the curve below. Zero is a mean center value. The highest and lowest values can be found in the right and left most parts of the curve.
Let's generate some sample data and get its z-scores.
set.seed(123)
x = sample(1:50, 100, replace=T)
Getting z-scores with a formula.
m = mean(x)
s = sd(x)
zs = (x - m)/s
summary(x)
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.00 16.00 25.50 26.21 36.25 50.00
summary(zs)
Min. 1st Qu. Median Mean 3rd Qu. Max.
-1.91447 -0.77536 -0.05392 0.00000 0.76245 1.80663
As summary shows, the x vector was centered into 0 mean value. In 'zs', the value of x vector's 1 is equal to -1.91, and 50 is equal to 1.8 sigma value.
In R, we can use scale() command to get z-scores.
scale(x)
[,1]
[1,] 0.28781591
[2,] -0.69941543
[3,] -0.09188846
........
[98,] 0.51563852
[99,] -1.38288328
[100,] 0.21187503
attr(,"scaled:center")
[1] 26.21
attr(,"scaled:scale")
[1] 13.16814
We need the first part of a scale function result.
sc_zs = scale(x)[,1] summary(sc_zs) Min. 1st Qu. Median Mean 3rd Qu. Max. -1.91447 -0.77536 -0.05392 0.00000 0.76245 1.80663
A summary shows that the result is the same as the one that was taken with a formula.
The scale function is often used to clean up data to remove the mean value from the series data.
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