Access these functions by checking Show Advanced in the Functions drop-down list.

## Table Functions versus Row Functions section_8977BE40A47E4ED79EB543A9703A4905

A table function is one where the output is the same for every row of the table. A row function is one where the output is different for every row of the table.

## What does the Include-Zeros parameter mean? section_C7A2B05929584C65B308FD372CB8E8E3

It tells whether to include zeros in the computation. Sometimes zero means “nothing”, but sometimes it’s important.

For example, if you have a Revenue metric, and then add a Page Views metric to the report, there are suddenly more rows for your revenue which are all zero. You probably don’t want this to affect any MEAN, MIN, QUARTILE, etc. calculations that you have on the revenue column. In this case, you would check the include-zeros parameter.

On the other hand, if you have two metrics that you are interested in, it may not be fair to say that one has a higher average or minimum because some of its rows were zeros, so you would not check the parameter to include the zeros.

Returns the value of its argument. Use NOT to make sure that a value is not equal to one particular value.

NOTE
0 (zero) means False, and any other value is True.
``````AND(logical_test1,[logical_test2],...)
``````
Argument
Description
logical_test1
Required. Any value or expression that can be evaluated to TRUE or FALSE.
logical_test2
Optional. Additional conditions that you want to evaluate as TRUE or FALSE

## Approximate Count Distinct (dimension) concept_000776E4FA66461EBA79910B7558D5D7

Returns the approximated distinct count of dimension items for the selected dimension. The function uses the HyperLogLog (HLL) method of approximating distinct counts.  It is configured to guarantee the value is within 5% of the actual value 95% of the time.

``````Approximate Count Distinct (dimension)
``````
Argument
dimension
The dimension for which you want the approximate distinct item count.

### Example Use Case section_424E3FC5092948F0A9D655F6CCBA0312

Approximate Count Distinct (customer ID eVar) is a common use case for this function.

Definition for a new ‘Approximate Customers’ calculated metric:

This is how the “Approximate Customers” metric could be used in reporting:

### Uniques Exceeded section_9C583858A9F94FF7BA054D1043194BAA

Like Count() and RowCount(), Approximate Count Distinct() is subject to “uniques exceeded” limits. If the “uniques exceeded” limit is reached within a particular month for a dimension, the value is counted as 1 dimension item.

### Comparing Count Functions section_440FB8FB44374459B2C6AE2DA504FC0B

Approximate Count Distinct() is an improvement over Count() and RowCount() functions because the metric created can be used in any dimensional report to render an approximated count of items for a separate dimension. For example, a count of customer IDs used in a Mobile Device Type report.

This function will be marginally less accurate than Count() and RowCount() because it uses the HLL method, whereas Count() and RowCount() are exact counts.

## Arc Cosine (Row) concept_1DA3404F3DDE4C6BAF3DBDD655D79C7B

Returns the arccosine, or inverse of the cosine, of a metric. The arccosine is the angle whose cosine is number. The returned angle is given in radians in the range 0 (zero) to pi. If you want to convert the result from radians to degrees, multiply it by 180/PI( ).

``````ACOS(metric)
``````
Argument
metric
The cosine of the angle you want from -1 to 1.

## Arc Sine (Row) concept_90F00DEC46BA47F8A21493647D9668CD

Returns the arcsine, or inverse sine, of a number. The arcsine is the angle whose sine is number. The returned angle is given in radians in the range -pi/2 to pi/2. To express the arcsine in degrees, multiply the result by 180/PI( ).

``````ASIN(metric)
``````
Argument
metric
The cosine of the angle you want from -1 to 1.

## Arc Tangent (Row) concept_3408520673774A10998E9BD8B909E90C

Returns the arctangent, or inverse tangent, of a number. The arctangent is the angle whose tangent is number. The returned angle is given in radians in the range -pi/2 to pi/2. To express the arctangent in degrees, multiply the result by 180/PI( ).

``````ATAN(metric)
``````
Argument
metric
The cosine of the angle you want from -1 to 1.

## Exponential Regression: Predicted Y (Row) concept_25615693312B4A7AB09A2921083502AD

Calculates the predicted y-values (metric_Y), given the known x-values (metric_X) using the “least squares” method for calculating the line of best fit based on .

``````ESTIMATE.EXP(metric_X, metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to designate as the independent data.
metric_Y
A metric that you would like to designate as the dependent data.

## Cdf-T concept_4E2F2673532A48B5AF786521DE428A66

Returns the percentage of values in a student’s t-distribution with n degrees of freedom that have a z-score less than x.

``````cdf_t( -∞, n ) = 0
cdf_t(  ∞, n ) = 1
cdf_t( 3, 5 ) ? 0.99865
cdf_t( -2, 7 ) ? 0.0227501
cdf_t( x, ∞ ) ? cdf_z( x )
``````

Returns the percentage of values in a normal distribution that have a z-score less than x.

``````cdf_z( -∞ ) = 0
cdf_z( ∞ ) = 1
cdf_z( 0 ) = 0.5
cdf_z( 2 ) ? 0.97725
cdf_z( -3 ) ? 0.0013499
``````

## Ceiling (Row) concept_A14CDB1E419B4AA18D335E5BA2548346

Returns the smallest integer not less than a given value. For example, if you want to avoid reporting currency decimals for revenue and a product has \$569.34, use the formula CEILING( Revenue) to round revenue up to the nearest dollar, or \$570.

``````CEILING(metric)
``````
Argument
Description
metric
The metric that you want to round.

## Cosine (Row) concept_DD07AA1FB08145DC89B69D704545FD0A

Returns the cosine of the given angle. If the angle is in degrees, multiply the angle by PI( )/180.

``````COS(metric)
``````
Argument
Description
metric
The angle in radians for which you want the cosine.

## Cube Root concept_BD93EFA45DF7447A8F839E1CA5B5F795

Returns the positive cube root of a number. The cube root of a number is the value of that number raised to the power of 1/3.

``````CBRT(metric)
``````
Argument
Description
metric
The metric for which you want the cube root.

## Cumulative concept_3D3347797B6344CE88B394C3E39318ED

Returns the sum of x for the last N rows (as ordered by the dimension, using hash values for string based fields).

If N <= 0 it uses all previous rows. Since it’s ordered by the dimension it’s only useful on dimensions that have a natural order like date or path length.

``````| Date | Rev  | cumul(0,Rev) | cumul(2,Rev) |
|------+------+--------------+--------------|
| May  | \$500 | \$500         | \$500         |
| June | \$200 | \$700         | \$700         |
| July | \$400 | \$1100        | \$600         |
``````

## Cumulative Average concept_ABB650962DC64FD58A79C305282D3E61

Returns the average of the last N rows.

If N <= 0 it uses all previous rows. Since it’s ordered by the dimension it’s only useful on dimensions that have a natural order like date or path length.

NOTE
This does not work as you might expect with rate metrics like revenue/visitor: it averages the rates instead of summing revenue over the last N and summing visitors over the last N and then dividing them. Instead, use
``````cumul(revenue)/cumul(visitor)
``````

## Equal concept_A3B97152B5F74E04A97018B35734BEEB

Returns items that match exactly for a numeric or string value.

## Exponential Regression_ Correlation Coefficient (Table) concept_C18BBFA43C1A499293290DF49566D8D8

Returns the correlation coefficient, r, between two metric columns ( metric_A and metric_B) for the regression equation .

``````CORREL.EXP(metric_X, metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to correlate with metric_Y.
metric_Y
A metric that you would like to correlate with metric_X.

## Exponential Regression: Intercept (Table) concept_0047206C827841AD936A3BE58EEE1514

Returns the intercept, b, between two metric columns ( metric_X and metric_Y) for

``````INTERCEPT.EXP(metric_X, metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to designate as the independent data.
metric_Y
A metric that you would like to designate as the dependent data.

## Exponential Regression: Slope (Table) concept_230991B0371E44308C52853EFA656F04

Returns the slope, a, between two metric columns ( metric_X and metric_Y) for .

``````SLOPE.EXP(metric_X, metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to designate as the independent data.
metric_Y
A metric that you would like to designate as the dependent data.

## Floor (Row) concept_D368150EC3684077B284EE471463FC31

Returns the largest integer not greater than a given value. For example, if you want to avoid reporting currency decimals for revenue and a product has \$569.34, use the formula FLOOR( Revenue) to round revenue down to the nearest dollar, or \$569.

``````FLOOR(metric)
``````
Argument
Description
metric
The metric you want to round.

## Greater Than concept_A83734A0C0C14646B76D2CC5E677C644

Returns items whose numeric count is greater than the value entered.

## Greater Than or Equal concept_8CA6DF1F84784D50849BF1C566AE1D37

Returns items whose numeric count is greater than or equal to the value entered.

## Hyperbolic Cosine (Row) concept_79DD5681CE9640BDBA3C3F527343CA98

Returns the hyperbolic cosine of a number.

``````COSH(metric)
``````
Argument
Description
metric
The angle in radians for which you want to find the hyperbolic cosine.

## Hyperbolic Sine (Row) concept_96230731600C45E3A4E823FE155ABA85

Returns the hyperbolic sine of a number.

``````SINH(metric)
``````
Argument
Description
metric
The angle in radians for which you want to find the hyperbolic sine.

## Hyperbolic Tangent (Row) concept_BD249013732F462B9863629D142BCA6A

Returns the hyperbolic tangent of a number.

``````TANH(metric)
``````
Argument
Description
metric
The angle in radians for which you want to find the hyperbolic tangent.

## IF (Row) concept_6BF0F3EAF3EF42C288AEC9A79806C48E

The IF function returns one value if a condition you specify evaluates to TRUE, and another value if that condition evaluates to FALSE.

``````IF(logical_test, [value_if_true], [value_if_false])
``````
Argument
Description
logical_test
Required. Any value or expression that can be evaluated to TRUE or FALSE.
[value_if_true]
The value that you want to be returned if the logical_test argument evaluates to TRUE. (This argument defaults to 0 if not included.)
[value_if_false]
The value that you want to be returned if the logical_test argument evaluates to FALSE. (This argument defaults to 0 if not included.)

Returns items whose numeric count is less than the value entered.

## Less Than or Equal concept_99D12154DE4848B1B0A6327C4322D288

Returns items whose numeric count is less than or equal to the value entered.

## Linear regression_ Correlation Coefficient concept_132AC6B3A55248AA9C002C1FBEB55C60

Y = a X + b. Returns the correlation coefficient

## Linear regression_ Intercept concept_E44A8D78B802442DB855A07609FC7E99

Y = a X + b. Returns b.

## Linear regression_ Predicted Y concept_9612B9BF106D4D278648D2DF92E98EFC

Y = a X + b. Returns Y.

## Linear regression_ Slope concept_12352982082A4DDF824366B073B4C213

Y = a X + b. Returns a.

## Log Base 10 (Row) concept_4C65DF9659164261BE52AA5A95FD6BC1

Returns the base-10 logarithm of a number.

``````LOG10(metric)
``````
Argument
Description
metric
The positive real number for which you want the base-10 logarithm.

## Log regression: Correlation coefficient (Table) concept_F3EB35016B754E74BE41766E46FDC246

Returns the correlation coefficient, r, between two metric columns (metric_X and metric_Y) for the regression equation Y = a ln(X) + b. It is calculated using the CORREL equation.

``````CORREL.LOG(metric_X,metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to correlate with metric_Y.
metric_Y
A metric that you would like to correlate with metric_X.

## Log regression: Intercept (Table) concept_75A3282EDF54417897063DC26D4FA363

Returns the intercept b as the least squares regression between two metric columns (metric_X and metric_Y) for the regression equation Y = a ln(X) + b. It is calculated using the INTERCEPT equation.

``````INTERCEPT.LOG(metric_X, metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to designate as the independent data.
metric_Y
A metric that you would like to designate as the dependent data.

## Log Regression: Predicted Y (Row) concept_5F3A9263BBB84E6098160A4DFB9E3607

Calculates the predicted y values (metric_Y), given the known x values (metric_X) using the “least squares” method for calculating the line of best fit based on Y = a ln(X) + b. It is calculated using the ESTIMATE equation.

In regression analysis, this function calculates the predicted y values (metric_Y), given the known x values (metric_X) using the logarithm for calculating the line of best fit for the regression equation Y = a ln(X) + b. The a values correspond to each x value, and b is a constant value.

``````ESTIMATE.LOG(metric_X, metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to designate as the independent data.
metric_Y
A metric that you would like to designate as the dependent data.

## Log regression: Slope (Table) concept_B291EFBE121446A6B3B07B262BBD4EF2

Returns the slope, a, between two metric columns (metric_X and metric_Y) for the regression equation Y = a ln(X) + b. It is calculated using the SLOPE equation.

``````SLOPE.LOG(metric_A, metric_B)
``````
Argument
Description
metric_A
A metric that you would like to designate as the independent data.
metric_B
A metric that you would like to designate as the dependent data.

## Natural Log concept_D3BE148A9B84412F8CA61734EB35FF9E

Returns the natural logarithm of a number. Natural logarithms are based on the constant e (2.71828182845904). LN is the inverse of the EXP function.

``````LN(metric)
``````
Argument
Description
metric
The positive real number for which you want the natural logarithm.

## NOT concept_BD954C455A8148A3904A301EC4DC821E

Returns 1 if the number is 0 or returns 0 if another number.

``````NOT(logical)
``````
Argument
Description
logical
Required. A value or expression that can be evaluated to TRUE or FALSE.

Using NOT requires knowing if the expressions (<, >, =, <> , etc.) return 0 or 1 values.

## Not equal concept_EC010B7A9D2049099114A382D662FC16

Returns all items that do not contain the exact match of the value entered.

## Or (Row) concept_AF81A33A376C4849A4C14F3A380639D2

Returns TRUE if any argument is TRUE, or returns FALSE if all arguments are FALSE.

NOTE
0 (zero) means False, and any other value is True.
``````OR(logical_test1,[logical_test2],...)
``````
Argument
Description
logical_test1
Required. Any value or expression that can be evaluated to TRUE or FALSE.
logical_test2
Optional. Additional conditions that you want to evaluate as TRUE or FALSE

## Pi concept_41258789660D4A33B5FB86228F12ED9C

Returns the constant PI, 3.14159265358979, accurate to 15 digits.

``````PI()
``````

The PIfunction has no arguments.

## Power regression: Correlation coefficient (Table) concept_91EC2CFB5433494F9E0F4FDD66C63766

Returns the correlation coefficient, r, between two metric columns (metric_X and metric_Y) for Y = b*X.

``````CORREL.POWER(metric_X, metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to correlate with metric_Y.
metric_Y
A metric that you would like to correlate with metric_X.

## Power regression: Intercept (Table) concept_7781C85597D64D578E19B212BDD1764F

Returns the intercept, b, between two metric columns (metric_X and metric_Y) for Y = b*X.

`````` INTERCEPT.POWER(metric_X, metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to designate as the independent data.
metric_Y
A metric that you would like to designate as the dependent data.

## Power regression: Predicted Y (Row) concept_CD652C0A921D4EFBA8F180CB8E486B18

Calculates the predicted y values ( metric_Y), given the known x values ( metric_X) using the “least squares” method for calculating the line of best fit for Y = b*X.

`````` ESTIMATE.POWER(metric_X, metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to designate as the independent data.
metric_Y
A metric that you would like to designate as the dependent data.

## Power regression: Slope (Table) concept_5B9E71B989234694BEB5EEF29148766C

Returns the slope, a, between two metric columns (metric_X and metric_Y) for Y = b*X.

``````SLOPE.POWER(metric_X, metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to designate as the independent data.
metric_Y
A metric that you would like to designate as the dependent data.

## Quadratic regression: Correlation coefficient (Table) concept_9C9101A456B541E69BA29FCEAC8CD917

Returns the correlation coefficient, r, between two metric columns (metric_X and metric_Y) for Y=(a X+b)***.

``````CORREL.QUADRATIC(metric_X, metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to correlate with metric_Y.
metric_Y
A metric that you would like to correlate with metric_X.

## Quadratic regression: Intercept (Table) concept_69DC0FD6D38C40E9876F1FD08EC0E4DE

Returns the intercept, b, between two metric columns (metric_X and metric_Y) for Y=(a X+b)***.

``````INTERCEPT.POWER(metric_X, metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to designate as the independent data.
metric_Y
A metric that you would like to designate as the dependent data.

## Quadratic regression: Predicted Y (Row) concept_2F1ED70B1BDE4664A61CC09D30C39CBB

Calculates the predicted y values (metric_Y), given the known x values (metric_X) using the least squares method for calculating the line of best fit using Y=(a X+b)*** .

``````ESTIMATE.QUADRATIC(metric_A, metric_B)
``````
Argument
Description
metric_A
A metric that you would like to designate as the independent data.
metric_B
A metric that you would like to designate as the dependent data.

## Quadratic regression: Slope (Table) concept_0023321DA8E84E6D9BCB06883CA41645

Returns the slope, a, between two metric columns (metric_X and metric_Y) for Y=(a X+b)***.

``````SLOPE.QUADRATIC(metric_X, metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to designate as the independent data.
metric_Y
A metric that you would like to designate as the dependent data.

## Reciprocal regression: Correlation coefficient (Table) concept_EBEC509A19164B8AB2DBDED62F4BA2A5

Returns the correlation coefficient, r, between two metric columns (metric_X) and metric_Y) for Y = a/X+b.

``````CORREL.RECIPROCAL(metric_X, metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to correlate with metric_Y.
metric_Y
A metric that you would like to correlate with metric_X.

## Reciprocal regression: Intercept (Table) concept_2DA45B5C69F140EC987649D2C88F19B3

Returns the intercept, b, between two metric columns (metric_X and metric_Y) for Y = a/X+b.

``````INTERCEPT.RECIPROCAL(metric_A, metric_B)
``````
Argument
Description
metric_X
A metric that you would like to designate as the independent data.
metric_Y
A metric that you would like to designate as the dependent data.

## Reciprocal regression: Predicted Y (Row) concept_2CF4B8F417A84FE98050FE488E227DF8

Calculates the predicted y values (metric_Y), given the known x values (metric_X) using the least squares method for calculating the line of best fit using Y = a/X+b.

``````ESTIMATE.RECIPROCAL(metric_X, metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to designate as the independent data.
metric_Y
A metric that you would like to designate as the dependent data.

## Reciprocal regression: Slope (Table) concept_8A8B68C9728E42A6BFDC6BD5CBDCCEC5

Returns the slope, a, between two metric columns (metric_X and metric_Y) for Y = a/X+b.

``````SLOPE.RECIPROCAL(metric_X, metric_Y)
``````
Argument
Description
metric_X
A metric that you would like to designate as the independent data.
metric_Y
A metric that you would like to designate as the dependent data.

## Sine (Row) concept_21C8C3AA835947A28B53A4E756A7451E

Returns the sine of the given angle. If the angle is in degrees, multiply the angle by PI( )/180.

``````SIN(metric)
``````
Argument
Description
metric
The angle in radians for which you want the sine.

## T-Score concept_80D2B4CED3D0426896B2412B4FC73BF7

Alias for Z-Score, namely the deviation from the mean divided by the standard deviation

Performs an m-tailed t-test with t-score of col and n degrees of freedom.

The signature is `t_test( x, n, m )`. Underneath, it simply calls `m*cdf_t(-abs(x),n)`. (This is similar to the z-test function which runs `m*cdf_z(-abs(x))`.

Here, `m` is the number of tails, and `n` is the degrees of freedom. These should be numbers (constant for the whole report, i.e. not changing on a row by row basis).

`X` is the t-test statistic, and would often be a formula (e.g. zscore) based on a metric and will be evaluated on every row.

The return value is the probability of seeing the test statistic x given the degrees of freedom and number of tails.

Examples:

1. Use it to find outliers:

code language-none
``````t_test( zscore(bouncerate), row-count-1, 2)
``````
2. Combine it with `if` to ignore very high or low bounce rates, and count visits on everything else:

code language-none
``````if ( t_test( z-score(bouncerate), row-count, 2) < 0.01, 0, visits )
``````

## Tangent concept_C25E00CB17054263AB0460D9EF94A700

Returns the tangent of the given angle. If the angle is in degrees, multiply the angle by PI( )/180.

``````TAN (metric)
``````
Argument
Description
metric
The angle in radians for which you want the tangent.

Returns the Z-score, or normal score, based upon a normal distribution. The Z-score is the number of standard deviations an observation is from the mean. A Z-score of 0 (zero) means the score is the same as the mean. A Z-score can be positive or negative, indicating whether it is above or below the mean and by how many standard deviations.

The equation for Z-score is:

where x is the raw score, μ is the mean of the population, and σ is the standard deviation of the population.

NOTE
μ (mu) andσ (sigma) are automatically calculated from the metric.

Z-score(metric)

Argument
Description
metric
Returns the value of its first non-zero argument.