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stats[transform, standardscore]

replace each item by its standard score

 

Calling Sequence

Parameters

Description

Examples

Calling Sequence

stats[transform, standardscore[n_constraints]](data)

transform[standardscore[n_constraints]](data)

Parameters

n_constraints

-

(optional, default=0) use 0 for population, 1 for sample

data

-

statistical list

Description

• 

Important: The stats package has been deprecated. Use the superseding package Statistics instead.

• 

The function standardscore of the subpackage stats[transform, ...] replaces each item in data by its standard score.

• 

The standard score of a quantity x is xmeanstandarddeviation, where mean and standarddeviation are the mean and the standard deviation  of data, respectively.

• 

Standard scores are also known as zscores, or z-scores.

• 

The quantity n_constraints  is explained in more detail in the description of stats[describe,standarddeviation].

• 

The standard score is very useful in comparing distributions. For example, a student can compare her relative standing between two courses if she knows her mark, the courses averages and standard deviations.

• 

Results expressed in terms of standard score are also known as being expressed in standard units.

• 

By definition, the set of standard scores of a list of statistical data will have mean equal to 0 and standard deviation equal to 1.

• 

Missing items remain unchanged. Weighted data and class data are recognized.

Examples

Important: The stats package has been deprecated. Use the superseding package Statistics instead.

withstats:

dataWeight3,10,missing,4,Weight11..12,3

data:=Weight3,10,missing,4,Weight11..12,3

(1)

The standard scores for the given data are

transform[standardscore]data:transform[apply[evalf]]

Weight3.,10,missing,4.,Weight11...12.,3

(2)

Here is another way of computing the standard scores.

transform[divideby[standarddeviation]]transform[subtractfrom[mean]]data

Weight5393859385,10,missing,518779385,Weight17193859385..19993859385,3

(3)

transform[apply[evalf]]

Weight0.5470899427,10,missing,0.2580612937,Weight1.765139249..2.054167898,3

(4)

And here is a third way.

the_sddescribe[standarddeviation]data

the_sd:=1289385

(5)

the_meandescribe[mean]data

the_mean:=13728

(6)

transform[apply[unapplyxthe_meanthe_sd,x]]data

Weight5393859385,10,missing,518779385,Weight17193859385..19993859385,3

(7)

transform[apply[evalf]]

Weight0.5470899427,10,missing,0.2580612937,Weight1.765139249..2.054167898,3

(8)

See Also

Statistics

transform(deprecated)[apply]

transform(deprecated)[multiapply]

transform(deprecated)[standardscore]

transform(deprecated)[subtractfrom]

 


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