## Introduction

There are many categories of DAX functions in Power BI. Statistical function is one of them. Statistical functions are used for computation. Below is the list of statistical functions and their descriptions.

## Function and description

## BETA.DIST

Returns the beta distribution. The beta distribution is commonly used to study variation in the percentage of something across samples, such as the fraction of the day people spend watching television.

Syntax- BETA.DIST(X, Alpha, Beta, Cumulative, [A], [B])

X-The value between A and B at which to evaluate the function

Alpha- A parameter of the distribution.

Beta -A parameter of the distribution.

A -It is optional. A lower bound to the interval of x.

B - It is optional. An upper bound to the interval of x.

## BETA.INV

Returns the inverse of the beta cumulative probability density function (BETA.DIST). If probability = BETA.DIST(x….TRUE) BETA.INV(Probability…)= x. The beta distribution can be used in project planning to model probable completion times given an expected completion times and variability.

Syntax- BETA.INV(Probability, Alpha, Beta, [A], [B])

Probability-A probability associated with the beta distribution.

Alpha- A parameter of the distribution.

Beta -A parameter of the distribution.

A - It is optional. A lower bound to the interval of x.

B - It is optional. An upper bound to the interval of x.

## CHISQ.DIST

Returns the chi-squared distribution.The chi-squared distribution is commonly used to study variation in the percentage of something across samples, such as the fraction of the day people spend watching television.

Syntax- CHISQ.DIST(X, Deg_freedom, cumulative)

X-Any value at which you want to calculate the distribution. It should be greater than zero.

Deg_freedom-Number of degrees of freedom. It should not less than 0.

Cumulative-A logical value that determines the form of the function. If it is TRUE, then function returns the cumulative distribution. In case of FALSE, it will return the probability density function.

**Ex.**

## CHISQ.DIST.RT

Returns the right-tailed probability of the chi-squared distribution. The chi-squared distribution is associated with a chi-squared test. Use the chi-squared test to compare observed and expected value. For example, a genetic experiment might hypothesize that the next generation of plants will exhibit a certain set of colors. By comparing the observed result with the expected ones, you can decide whether your original hypothesis is valid.

Syntax- CHISQ.DIST.RT(X, Deg_freedom)

X- Any value at which you want to calculate the distribution. It should be greater than zero.

Deg_freedom- Number of degrees of freedom. It should not less than 0.

**Ex.**

## CHISQ.INV

Returns the inverse of the left-tailed probability of the chi-squared distribution. The chi-squared distribution is commonly used to study variation in the percentage of something across samples, such as the fraction of the day people spend watching television.

Syntax-CHISQ.INV(Probability,Deg_freedom)

Probability- A probability associated with the chi-squared distribution.

Deg_freedom- Number of degrees of freedom. It should not less than 0.

**Ex.**

## CHISQ.INV.RT

Returns the inverse of the right-tailed probability of the chi-squared distribution. If Probability= CHISQ.DIST.RT(X…), then CHISQ.INV.RT(probability..)=X. Use this function to compare observed result with expected one in order to decide whether your original hypothetic is valid.

Syntax- CHISQ.INV.RT(Probability, Deg_freedom)

Probability- A probability associated with the chi-squared distribution.

Deg_freedom- Number of degrees of freedom. It should not less than 0.

**Ex.**

## CONFIDENCE.NORM

Returns the confidence interval for a population mean, using a normal distribution.

Syntax- CONFIDENCE.NORM(Alpha, Standard_dev, Size)

Alpha-The significance level used to compute the confidence level. The confidence level equals 100*(1 - alpha)%, or in other words, an alpha of 0.05 indicates a 95 percent confidence level.

Standard_dev-The population standard deviation for the data range and is assumed to be known.

Size- The sample size.

**Note**

This function is not supported for use in DirectQuery mode when used in calculated columns or row-level security (RLS) rules.

## CONFIDENCE.T

Returns the confidence interval for a population mean, using a student’s distribution.

Syntax- CONFIDENCE.T(Alpha, Standard_dev, Size)

Alpha- The significance level used to compute the confidence level. The confidence level equals 100*(1 - alpha)%, or in other words, an alpha of 0.05 indicates a 95 percent

confidence level.

Standard_dev- The population standard deviation for the data range and is assumed to be known.

Size- The sample size.

**Ex.**

## DATATABLE

Returns a table with data defined inline.

Syntax- DATATABLE(Name1, Type1, …, Data)

Name1- Any DAX expression that returns a table.

Type1-An enumeration that includes: INTEGER, DOUBLE, STRING, BOOLEAN, CURRENCY, DATETIME

Data - A single argument using Excel syntax for a one-dimensional array constant, nested to provide an array of arrays. This argument represents the set of data values that will be in the table

**Ex.**

## EXPON.DIST

Returns the exponentialdistribution.Use EXPON.DIST to model the time between events, such as how long an automated bank teller takes to deliver cash. For example, you can use EXPON.DIST to determine the probability that the process takes at most 1 minute.

Syntax –EXPON.DIST(X, Lambda, Cumulative)

X - Required. The value of the function.

Lambda – Required. The parameter values.

Cumulative - Required. A logical value that indicates which form of the exponential function to provide. If cumulative is TRUE, EXPON.DIST returns the cumulative distribution function; if FALSE, it returns the probability density function.

__Note__This function is not supported for use in DirectQuery mode when used in calculated columns or row-level security (RLS) rules.

## GEOMEAN

Returns geometric mean of given column reference.

Syntax –GEOMEAN(ColumnName)

ColumnName –Column that contains number for which the geometric mean is to be computed.

**Ex.**

## GEOMEANX

Returns geometric mean of an expression value in table.

Syntax- GEOMEANX (Table, Expression)

Table- The table containing the rows for which the expression will be evaluated.

Expression- The expression to be evaluated for each row of the table.

**Ex.**

## Summary

This was the simple description of some statistical functions. Hope you understand these functions and their uses. Thanks for reading this article. Have a nice day.