# Statistical Functions Part Five

## WEIBULL.DIST

Returns the values of the Weibull distribution.

The Weibull distribution is a continuous probability distribution, with parameters Alpha > 0 (shape) and Beta > 0 (scale).

If C is 0, WEIBULL.DIST calculates the probability density function.

If C is 1, WEIBULL.DIST calculates the cumulative distribution function.

### Syntax

`WEIBULL.DIST(Number; Alpha; Beta; C)`

Number is the value at which to calculate the Weibull distribution.

Alpha is the shape parameter of the Weibull distribution.

Beta is the scale parameter of the Weibull distribution.

C indicates the type of function.

### Example

`=WEIBULL.DIST(2;1;1;1)` returns 0.8646647168.

## WEIBULL

Returns the values of the Weibull distribution.

The Weibull distribution is a continuous probability distribution, with parameters Alpha > 0 (shape) and Beta > 0 (scale).

If C is 0, WEIBULL calculates the probability density function.

If C is 1, WEIBULL calculates the cumulative distribution function.

### Syntax

`WEIBULL(Number; Alpha; Beta; C)`

Number is the value at which to calculate the Weibull distribution.

Alpha is the shape parameter of the Weibull distribution.

Beta is the scale parameter of the Weibull distribution.

C indicates the type of function.

### Example

`=WEIBULL(2;1;1;1)` returns 0.86.

## VARPA

Calculates the variance based on the entire population. The value of text is 0.

### Syntax

`VARPA(Value1; Value2; ...Value30)`

Value1,value2,...Value30 are values or ranges representing an entire population.

### Example

`=VARPA(A1:A50)`

## VARP

Calculates a variance based on the entire population.

### Syntax

`VARP(Number1; Number2; ...Number30)`

Number1, Number2, ...Number30 are numerical values or ranges representing an entire population.

### Example

`=VARP(A1:A50)`

## VAR.S

Estimates the variance based on a sample.

### Syntax

`VAR.S(Number1; Number2; ...Number30)`

Number1, Number2, ...Number30 are numerical values or ranges representing a sample based on an entire population.

### Example

`=VAR.S(A1:A50)`

## VAR.P

Calculates a variance based on the entire population.

### Syntax

`VAR.P(Number1; Number2; ...Number30)`

Number1, Number2, ...Number30 are numerical values or ranges representing an entire population.

### Example

`=VAR.P(A1:A50)`

## VARA

Estimates a variance based on a sample. The value of text is 0.

### Syntax

`VARA(Value1; Value2; ...Value30)`

Value1, Value2,...Value30 are values or ranges representing a sample derived from an entire population. Text has the value 0.

### Example

`=VARA(A1:A50)`

## VAR

Estimates the variance based on a sample.

### Syntax

`VAR(Number1; Number2; ...Number30)`

Number1, Number2, ...Number30 are numerical values or ranges representing a sample based on an entire population.

### Example

`=VAR(A1:A50)`

## TTEST

Returns the probability associated with a Student's t-Test.

### Syntax

`TTEST(Data1; Data2; Mode; Type)`

Data1 is the dependent array or range of data for the first record.

Data2 is the dependent array or range of data for the second record.

Mode = 1 calculates the one-tailed test, Mode = 2 the two- tailed test.

Type is the kind of t-test to perform. Type 1 means paired. Type 2 means two samples, equal variance (homoscedastic). Type 3 means two samples, unequal variance (heteroscedastic).

### Example

`=TTEST(A1:A50;B1:B50;2;2)`

## TINV

Returns the inverse of the t-distribution.

### Syntax

`TINV(Number; DegreesFreedom)`

Number is the probability associated with the two-tailed t-distribution.

DegreesFreedom is the number of degrees of freedom for the t-distribution.

### Example

`=TINV(0.1;6)` returns 1.94

## T.TEST

Returns the probability associated with a Student's t-Test.

### Syntax

`T.TEST(Data1; Data2; Mode; Type)`

Data1 is the dependent array or range of data for the first record.

Data2 is the dependent array or range of data for the second record.

Mode = 1 calculates the one-tailed test, Mode = 2 the two- tailed test.

Type is the kind of t-test to perform. Type 1 means paired. Type 2 means two samples, equal variance (homoscedastic). Type 3 means two samples, unequal variance (heteroscedastic).

### Example

`=T.TEST(A1:A50;B1:B50;2;2)`

## T.INV.2T

Calculates the inverse of the two-tailed Student's T Distribution , which is a continuous probability distribution that is frequently used for testing hypotheses on small sample data sets.

### Syntax

`T.INV.2T(Number; DegreesFreedom)`

Number is the probability associated with the two-tailed t-distribution.

DegreesFreedom is the number of degrees of freedom for the t-distribution.

### Example

`=T.INV.2T(0.25; 10)` returns 1.221255395.

## T.INV

Returns the one tailed inverse of the t-distribution.

### Syntax

`T.INV(Number; DegreesFreedom)`

Number is the probability associated with the one-tailed t-distribution.

DegreesFreedom is the number of degrees of freedom for the t-distribution.

### Example

`=T.INV(0.1;6)` returns -1.4397557473.

## T.DIST.RT

Calculates the right-tailed Student's T Distribution, which is a continuous probability distribution that is frequently used for testing hypotheses on small sample data sets.

### Syntax

`T.DIST.RT(Number; DegreesFreedom)`

Number is the value for which the t-distribution is calculated.

DegreesFreedom is the number of degrees of freedom for the t-distribution.

### Example

`=T.DIST.RT(1; 10)` returns 0.1704465662.

## T.DIST.2T

Calculates the two-tailed Student's T Distribution, which is a continuous probability distribution that is frequently used for testing hypotheses on small sample data sets.

### Syntax

`T.DIST.2T(Number; DegreesFreedom)`

Number is the value for which the t-distribution is calculated.

DegreesFreedom is the number of degrees of freedom for the t-distribution.

### Example

`=T.DIST.2T(1; 10)` returns 0.3408931323.

## T.DIST

Returns the t-distribution.

### Syntax

`T.DIST(Number; DegreesFreedom; Cumulative)`

Number is the value for which the t-distribution is calculated.

DegreesFreedom is the number of degrees of freedom for the t-distribution.

Cumulative = 0 or FALSE returns the probability density function, 1 or TRUE returns the cumulative distribution function.

### Example

`=T.DIST(1; 10; TRUE)` returns 0.8295534338

## TDIST

Returns the t-distribution.

### Syntax

`TDIST(Number; DegreesFreedom; Mode)`

Number is the value for which the t-distribution is calculated.

DegreesFreedom is the number of degrees of freedom for the t-distribution.

Mode = 1 returns the one-tailed test, Mode = 2 returns the two-tailed test.

### Example

`=TDIST(12;5;1)`

## STEYX

Returns the standard error of the predicted y value for each x in the regression.

### Syntax

`STEYX(DataY; DataX)`

DataY is the array or matrix of Y data.

DataX is the array or matrix of X data.

### Example

`=STEYX(A1:A50;B1:B50)`

## STDEVPA

Calculates the standard deviation based on the entire population.

### Syntax

`STDEVPA(Value1;Value2;...Value30)`

Value1,value2,...value30 are values or ranges representing an entire population. Text has the value 0.

### Example

`=STDEVPA(A1:A50)` returns the standard deviation of the data referenced.

## STDEVP

Calculates the standard deviation based on the entire population.

### Syntax

`STDEVP(Number1;Number2;...Number30)`

Number 1,Number 2,...Number 30 are numerical values or ranges representing an entire population.

### Example

`=STDEVP(A1:A50)` returns a standard deviation of the data referenced.

## STDEV.S

Calculates the standard deviation based on sample of the population.

### Syntax

`STDEV.S(Number1;Number2;...Number30)`

Number 1,Number 2,...Number 30 are numerical values or ranges representing a sample of the population.

### Example

`=STDEV.S(A1:A50)` returns a standard deviation of the data referenced.

## STDEV.P

Calculates the standard deviation based on the entire population.

### Syntax

`STDEV.P(Number1;Number2;...Number30)`

Number 1,Number 2,...Number 30 are numerical values or ranges representing an entire population.

### Example

`=STDEV.P(A1:A50)` returns a standard deviation of the data referenced.

## STDEVA

Calculates the standard deviation of an estimation based on a sample.

### Syntax

`STDEVA(Value1;Value2;...Value30)`

Value1, Value2, ...Value30 are values or ranges representing a sample derived from an entire population. Text has the value 0.

### Example

`=STDEVA(A1:A50)` returns the estimated standard deviation based on the data referenced.

## STDEV

Estimates the standard deviation based on a sample.

### Syntax

`STDEV(Number1; Number2; ...Number30)`

Number1, Number2, ... Number30 are numerical values or ranges representing a sample based on an entire population.

### Example

`=STDEV(A1:A50)` returns the estimated standard deviation based on the data referenced.

## STANDARDIZE

Converts a random variable to a normalized value.

### Syntax

`STANDARDIZE(Number; Mean; StDev)`

Number is the value to be standardized.

Mean is the arithmetic mean of the distribution.

StDev is the standard deviation of the distribution.

### Example

`=STANDARDIZE(11;10;1)` returns 1. The value 11 in a normal distribution with a mean of 10 and a standard deviation of 1 is as much above the mean of 10, as the value 1 is above the mean of the standard normal distribution.

## SLOPE

Returns the slope of the linear regression line. The slope is adapted to the data points set in the y and x values.

### Syntax

`SLOPE(DataY; DataX)`

DataY is the array or matrix of Y data.

DataX is the array or matrix of X data.

### Example

`=SLOPE(A1:A50;B1:B50)`

## SKEWP

Calculates the skewness of a distribution using the population of a random variable.

`SKEWP(Number1; Number2;..., Number30)`

Number1, Number2, ..., Number30 are up to 30 numerical values or ranges.

Calculates the skewness of a distribution using the population, i.e. the possible outcomes, of a random variable. The sequence shall contain three numbers at least.

`SKEWP(2;3;1;6;8;5)` returns 0.2828158928

`SKEWP(A1:A6)` returns 0.2828158928, when the range A1:A6 contains {2;3;1;6;8;5}

`SKEWP(Number1;Number2)` returns zero always, if Number1 and Number2 results in two numbers.

`SKEWP(Number1)` returns Err:502 (Invalid argument) if Number1 results in one number, because SKEWP cannot be calculated with one value.

## SKEW

Returns the skewness of a distribution.

### Syntax

`SKEW(Number1; Number2; ...Number30)`

Number1, Number2...Number30 are numerical values or ranges.

### Example

`=SKEW(A1:A50)` calculates the value of skew for the data referenced.

## RANK.EQ

Returns the statistical rank of a given value, within a supplied array of values. If there are duplicate values in the list, these are given the same rank.

 The difference between `RANK.AVG` and `RANK.EQ` occurs when there are duplicates in the list of values. The `RANK.EQ` function returns the lower rank, whereas the `RANK.AVG` function returns the average rank.

### Syntax

`RANK.EQ(Value; Data; Type)`

Value is the value, whose rank is to be determined.

Data is the array or range of data in the sample.

Type (optional) is the sequence order.

Type = 0 means descending from the last item of the array to the first (this is the default),

Type = 1 means ascending from the first item of the range to the last.

### Example

`=RANK.EQ(A10;A1:A50)` returns the ranking of the value in A10 in value range A1:A50. If `Value` does not exist within the range an error message is displayed.

## RANK.AVG

Returns the statistical rank of a given value, within a supplied array of values. If there are duplicate values in the list, the average rank is returned.

 The difference between `RANK.AVG` and `RANK.EQ` occurs when there are duplicates in the list of values. The `RANK.EQ` function returns the lower rank, whereas the `RANK.AVG` function returns the average rank.

### Syntax

`RANK.AVG(Value; Data; Type)`

Value is the value, whose rank is to be determined.

Data is the array or range of data in the sample.

Type (optional) is the sequence order.

Type = 0 means descending from the last item of the array to the first (this is the default),

Type = 1 means ascending from the first item of the range to the last.

### Example

`=RANK.AVG(A10;A1:A50)` returns the ranking of the value in A10 in value range A1:A50. If `Value` does not exist within the range an error message is displayed.

## RANK

Returns the rank of a number in a sample.

### Syntax

`RANK(Value; Data; Type)`

Value is the value, whose rank is to be determined.

Data is the array or range of data in the sample.

Type (optional) is the sequence order.

Type = 0 means descending from the last item of the array to the first (this is the default),

Type = 1 means ascending from the first item of the range to the last.

### Example

`=RANK(A10;A1:A50)` returns the ranking of the value in A10 in value range A1:A50. If `Value` does not exist within the range an error message is displayed.

## PROB

Returns the probability that values in a range are between two limits. If there is no `End` value, this function calculates the probability based on the principle that the Data values are equal to the value of `Start`.

### Syntax

`PROB(Data; Probability; Start; End)`

Data is the array or range of data in the sample.

Probability is the array or range of the corresponding probabilities.

Start is the start value of the interval whose probabilities are to be summed.

End (optional) is the end value of the interval whose probabilities are to be summed. If this parameter is missing, the probability for the Start value is calculated.

### Example

`=PROB(A1:A50;B1:B50;50;60)` returns the probability with which a value within the range of A1:A50 is also within the limits between 50 and 60. Every value within the range of A1:A50 has a probability within the range of B1:B50.

## PERMUTATIONA

Returns the number of permutations for a given number of objects (repetition allowed).

### Syntax

`PERMUTATIONA(Count1; Count2)`

Count1 is the total number of objects.

Count2 is the number of objects in each permutation.

### Example

How often can 2 objects be selected from a total of 11 objects?

`=PERMUTATIONA(11;2)` returns 121.

`=PERMUTATIONA(6;3)` returns 216. There are 216 different possibilities to put a sequence of 3 playing cards together out of six playing cards if every card is returned before the next one is drawn.

## PERMUT

Returns the number of permutations for a given number of objects.

### Syntax

`PERMUT(Count1; Count2)`

Count1 is the total number of objects.

Count2 is the number of objects in each permutation.

### Example

`=PERMUT(6;3)` returns 120. There are 120 different possibilities, to pick a sequence of 3 playing cards out of 6 playing cards.

## NORMSINV

Returns the inverse of the standard normal cumulative distribution.

### Syntax

`NORMSINV(Number)`

Number is the probability to which the inverse standard normal distribution is calculated.

### Example

`=NORMSINV(0.908789)` returns 1.3333.

## NORMSDIST

Returns the standard normal cumulative distribution function. The distribution has a mean of zero and a standard deviation of one.

It is GAUSS(x)=NORMSDIST(x)-0.5

### Syntax

`NORMSDIST(Number)`

Number is the value to which the standard normal cumulative distribution is calculated.

### Example

`=NORMSDIST(1)` returns 0.84. The area below the standard normal distribution curve to the left of X value 1 is 84% of the total area.

## NORM.S.INV

Returns the inverse of the standard normal cumulative distribution.

### Syntax

`NORM.S.INV(Number)`

Number is the probability to which the inverse standard normal distribution is calculated.

### Example

`=NORM.S.INV(0.908789)` returns 1.333334673.

## NORM.S.DIST

Returns the standard normal cumulative distribution function. The distribution has a mean of zero and a standard deviation of one.

### Syntax

`NORM.S.DIST(Number; Cumulative)`

Number is the value to which the standard normal cumulative distribution is calculated.

Cumulative 0 or FALSE calculates the probability density function. Any other value or TRUE calculates the cumulative distribution function.

### Examples

`=NORM.S.DIST(1;0)` returns 0.2419707245.

`=NORM.S.DIST(1;1)` returns 0.8413447461. The area below the standard normal distribution curve to the left of X value 1 is 84% of the total area.

## FORECAST.LINEAR

Extrapolates future values based on existing x and y values.

### Syntax

`FORECAST.LINEAR(Value; DataY; DataX)`

Value is the x value, for which the y value on the linear regression is to be returned.

DataY is the array or range of known y's.

DataX is the array or range of known x's.

### Example

`=FORECAST.LINEAR(50;A1:A50;B1;B50)` returns the Y value expected for the X value of 50 if the X and Y values in both references are linked by a linear trend.

## FORECAST

Extrapolates future values based on existing x and y values.

### Syntax

`FORECAST(Value; DataY; DataX)`

Value is the x value, for which the y value on the linear regression is to be returned.

DataY is the array or range of known y's.

DataX is the array or range of known x's.

### Example

`=FORECAST(50;A1:A50;B1;B50)` returns the Y value expected for the X value of 50 if the X and Y values in both references are linked by a linear trend.

## DEVSQ

Returns the sum of squares of deviations based on a sample mean.

### Syntax

`DEVSQ(Number1; Number2; ...Number30)`

Number1, Number2, ...Number30 numerical values or ranges representing a sample.

### Example

`=DEVSQ(A1:A50)`

## Related Topics

Functions by Category