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# VAR Function

Computes the variance among all values in a column. Input column can be of Integer or Decimal. If no numeric values are detected in the input column, the function returns 0.

The variance of a set of values attempts to measure the spread in values around the mean. A variance of zero means that all values are the same, and a small variance means that the values are closely bunched together. A high value for variance indicates that the numbers are spread out widely. Variance is always a positive value.

Var(X) = [Sum ((X - mean(X))2)] / Count(X)

If a row contains a missing or null value, it is not factored into the calculation.

Terms...

Relevant terms:

Term

Description

Population

Population statistical functions are computed from all possible values. See https://en.wikipedia.org/wiki/Statistical_population.

Sample

Sample-based statistical functions are computed from a subset or sample of all values. See https://en.wikipedia.org/wiki/Sampling_(statistics).

These function names include SAMP in their name.

Anmerkung

Statistical sampling has no relationship to the samples taken within the product. When statistical functions are computed during job execution, they are applied across the entire dataset. Sample method calculations are computed at that time.

• This function is calculated across the entire population.

• For more information on a sampled version of this function, see VARSAMP Function.

The square root of variance is standard deviation, which is used to measure variance under the assumption of a bell curve distribution. See STDEV Function.

For a version of this function computed over a rolling window of rows, see ROLLINGVAR Function.

Wrangle vs. SQL: This function is part of Wrangle, a proprietary data transformation language. Wrangle is not SQL. For more information, see Wrangle Language.

## Basic Usage

var(myRating)

Output: Returns the variance of the group of values from the myRating column.

## Syntax and Arguments

var(function_col_ref) [group:group_col_ref] [limit:limit_count]

Argument

Required?

Data Type

Description

function_col_ref

Y

string

Name of column to which to apply the function

For more information on the group and limit parameters, see Pivot Transform.

For more information on syntax standards, see Language Documentation Syntax Notes.

### function_col_ref

Name of the column the values of which you want to calculate the variance. Column must contain Integer or Decimal values.

• Literal values are not supported as inputs.

• Multiple columns and wildcards are not supported.

Usage Notes:

Required?

Data Type

Example Value

Yes

String (column reference)

myValues

## Examples

Tipp

For additional examples, see Common Tasks.

This example illustrates how you can apply statistical functions to your dataset. Calculations include average (mean), max, min, standard deviation, and variance.

Functions:

Item

Description

AVERAGE Function

Computes the average (mean) from all row values in a column or group. Input column can be of Integer or Decimal.

MIN Function

Computes the minimum value found in all row values in a column. Input column can be of Integer, Decimal or Datetime.

MAX Function

Computes the maximum value found in all row values in a column. Inputs can be Integer, Decimal, or Datetime.

VAR Function

Computes the variance among all values in a column. Input column can be of Integer or Decimal. If no numeric values are detected in the input column, the function returns 0.

STDEV Function

Computes the standard deviation across all column values of Integer or Decimal type.

NUMFORMAT Function

Formats a numeric set of values according to the specified number formatting. Source values can be a literal numeric value, a function returning a numeric value, or reference to a column containing an Integer or Decimal values.

MODE Function

Computes the mode (most frequent value) from all row values in a column, according to their grouping. Input column can be of Integer, Decimal, or Datetime type.

Source:

Students took a test and recorded the following scores. You want to perform some statistical analysis on them:

Student

Score

Anna

84

Ben

71

Caleb

76

Danielle

87

Evan

85

Faith

92

Gabe

85

Hannah

99

Ian

73

Jane

68

Transformation:

You can use the following transformations to calculate the average (mean), minimum, and maximum scores:

 Transformation Name New formula Single row formula AVERAGE(Score) 'avgScore'
 Transformation Name New formula Single row formula MIN(Score) 'minScore'
 Transformation Name New formula Single row formula MAX(Score) 'maxScore'

To apply statistical functions to your data, you can use the VAR and STDEV functions, which can be used as the basis for other statistical calculations.

 Transformation Name New formula Single row formula VAR(Score) var_Score
 Transformation Name New formula Single row formula STDEV(Score) stdev_Score

For each score, you can now calculate the variation of each one from the average, using the following:

 Transformation Name New formula Single row formula ((Score - avg_Score) / stdev_Score) 'stDevs'

Now, you want to apply grades based on a formula:

Grade

standard deviations from avg (stDevs)

A

stDevs > 1

B

stDevs > 0.5

C

-1 <= stDevs <= 0.5

D

stDevs < -1

F

stDevs < -2

You can build the following transformation using the IF function to calculate grades.

 Transformation Name New formula Single row formula IF((stDevs > 1),'A',IF((stDevs < -2),'F',IF((stDevs < -1),'D',IF((stDevs > 0.5),'B','C'))))

To clean up the content, you might want to apply some formatting to the score columns. The following reformats the stdev_Score and stDevs columns to display two decimal places:

 Transformation Name Edit column with formula stdev_Score NUMFORMAT(stdev_Score, '##.00')
 Transformation Name Edit column with formula stDevs NUMFORMAT(stDevs, '##.00')
 Transformation Name New formula Single row formula MODE(Score) 'modeScore'

Results:

Student

Score

modeScore

avgScore

minScore

maxScore

var_Score

stdev_Score

stDevs

Grade

Anna

84

85

82

68

99

87.00000000000001

9.33

0.21

C

Ben

71

85

82

68

99

87.00000000000001

9.33

-1.18

D

Caleb

76

85

82

68

99

87.00000000000001

9.33

-0.64

C

Danielle

87

85

82

68

99

87.00000000000001

9.33

0.54

B

Evan

85

85

82

68

99

87.00000000000001

9.33

0.32

C

Faith

92

85

82

68

99

87.00000000000001

9.33

1.07

A

Gabe

85

85

82

68

99

87.00000000000001

9.33

0.32

C

Hannah

99

85

82

68

99

87.00000000000001

9.33

1.82

A

Ian

73

85

82

68

99

87.00000000000001

9.33

-0.96

C

Jane

68

85

82

68

99

87.00000000000001

9.33

-1.50

D