Sterr - chart function

Sterr() finds the value of the standard error of the mean, (stdev/sqrt(n)), for the series of values aggregated in the expression iterated over the chart dimensions.

Syntax:  

Sterr([{SetExpression}] [DISTINCT] [TOTAL [<fld{, fld}>]] expr)

Return data type: numeric

Arguments:  

Argument Description
expr The expression or field containing the data to be measured.
SetExpression By default, the aggregation function will aggregate over the set of possible records defined by the selection. An alternative set of records can be defined by a set analysis expression.
DISTINCT If the word DISTINCT occurs before the function arguments, duplicates resulting from the evaluation of the function arguments are disregarded.
TOTAL

If the word TOTAL occurs before the function arguments, the calculation is made over all possible values given the current selections, and not just those that pertain to the current dimensional value, that is, it disregards the chart dimensions.

The TOTAL qualifier may be followed by a list of one or more field names within angle brackets <fld>. These field names should be a subset of the chart dimension variables.

See: Defining the aggregation scope

Limitations:  

The expression must not contain aggregation functions, unless these inner aggregations contain the TOTAL qualifier. For more advanced nested aggregations, use the advanced aggregation function Aggr, in combination with calculated dimensions.

Text values, NULL values and missing values are disregarded.

Examples and results:  

Add the example script to your app and run it. Then build a straight table with Type as dimension and Sterr(Value) as measure.

Totals should be enabled in the properties of the table.

Example Result

Sterr(Value)

Table1:

crosstable LOAD recno() as ID, * inline [

Observation|Comparison

35|2

40|27

12|38

15|31

21|1

14|19

46|1

10|34

28|3

48|1

16|2

30|3

32|2

48|1

31|2

22|1

12|3

39|29

19|37

25|2 ] (delimiter is '|');

The results of the Sterr(Value) calculation are:

  • Total is 2.4468583
  • Comparison is 3.2674431
  • Observation is 2.7968733

Did this information help you?

Thanks for letting us know. Is there anything you'd like to tell us about this topic?

Can you tell us why it did not help you and how we can improve it?