# Statistical Functions

- AVEDEV
Calculates the average of the magnitudes of deviations of data from a dataset's mean.

AVEDEV(1,2,3,4,5,6,7,8,9,10)Try this examplevalue1 (number)The first value or range of the sample.value2 (number)Additional values or ranges to include in the sample. - AVERAGE
The AVERAGE function returns the numerical average value in a dataset, ignoring text.

AVERAGE(1,2,3,4,5)Try this examplevalue1 (number)The first value or range to consider when calculating the average value.value2 (number)Additional values or ranges to consider when calculating the average value. - AVERAGEA
Returns the numerical average value in a dataset.

AVERAGEA(1,2,3,4,5)Try this examplevalue1 (number)The first value or range to consider when calculating the average value.value2 (number)Additional values or ranges to consider when calculating the average value. - AVERAGEIF
Returns the average of a range depending on criteria.

AVERAGEIF({var1}, "<10")Try this examplecriteria_range (range)The range to check against criterion.criterion (string)The pattern or test to apply to criteria_range. - BETA_DIST
Returns the probability of a given value as defined by the beta distribution function.

BETA_DIST(0.65, 1.234, 7, 0.5, 3)Try this examplevalue (number)The value at which to evaluate the probability function.alpha (number)The first shape parameter of the distribution. - BETA_INV
Returns the value of the inverse beta distribution function for a given probability.

BETA_INV(0.65,1.234,7,1,3)Try this exampleprobability (number)The probability at which to evaluate the function.alpha (number)The first shape parameter of the distribution. - BETADIST
The BETADIST function returns the probability of a given value as defined by the beta distribution function.

BETADIST(0.42, 3, 8)Try this examplevalue (number)The value at which to evaluate the probability function. The given value must be a number from the given lower boundary to the given upper boundary.alpha (number)The first shape parameter of the distribution. The given alpha must be a positive number. - BETAINV
The BETAINV function returns the value of the inverse beta distribution function for a given probability.

BETAINV(0.3,5,1)Try this exampleprobability (number)The probability at which to evaluate the function. Must be between 0 and 1, inclusive.alpha (number)The first shape parameter of the distribution. Must be positive. - BINOM_DIST
Calculates the probability of drawing a certain number of successes (or a maximum number of successes) in a certain number of tries given a population of a certain size containing a certain number of successes, with replacement of draws.

BINOMDIST(4,100,0.005,FALSE)Try this examplenum_successes (number)The number of successes for which to calculate the probability in num_trials trials.num_trials (number)The number of independent trials. - BINOMDIST
Calculates the probability of drawing a certain number of successes (or a maximum number of successes) in a certain number of tries given a population of a certain size containing a certain number of successes, with replacement of draws.

BINOMDIST(4,100,0.005,FALSE)Try this examplenum_successes (number)The number of successes for which to calculate the probability in num_trials trials.num_trials (number)The number of independent trials. - CHIDIST
Calculates the right-tailed chi-squared distribution, often used in hypothesis testing.

CHIDIST(3.45, 2)Try this examplex (number)The input to the chi-squared probability distribution function. The value at which to evaluate the function. Must be a positive number.degrees_freedom (number)The number of degrees of freedom of the distribution. - CHIINV
Calculates the inverse of the right-tailed chi-squared distribution.

CHIINV(0.42, 2)Try this exampleprobability (number)The probability associated with the right-tailed chi-squared distribution. Must be greater than 0 and less than 1.degrees_freedom (number)The number of degrees of freedom of the distribution. - CONFIDENCE
Calculates the width of half of the confidence interval for a normal distribution.

CONFIDENCE(0.05, 1.6, 250)Try this examplealpha (number)One minus the desired confidence level, e.g. 0.1 for 0.9, or 90%, confidence.standard_deviation (number)The standard deviation of the population. - CORREL
Calculates r, the Pearson product-moment correlation coefficient of a dataset.

CORREL({var1},{var2})Try this exampledata_y (array)The range representing the array or matrix of dependent data.data_x (array)The range representing the array or matrix of independent data. - COUNT
Returns the number of numeric values in a dataset.

COUNT(1,2,3,4,5)Try this examplevalue1 (number)The first value or range to consider when counting.value2 (number)Optional: Additional values or ranges to consider when counting. - COUNTA
Returns the number of values in a dataset.

COUNTA(1,2,3,4,5)Try this examplevalue1 (number)The first value or range to consider when counting.value2 (number)Optional: Additional values or ranges to consider when counting. - COUNTBLANK
Returns the number of empty variables in a given range.

COUNTBLANK({var1})Try this examplevalue1 (array)The first value or range in which to count the number of blanks.value2 (array)Optional - Additional values or ranges in which to count the number of blanks. - COUNTIF
Returns a conditional count across a range.

COUNTIF({var1},">20")Try this examplerange (array)The range that is tested against criterion.criterion (string)The pattern or test to apply to range. - COVAR
Calculates the covariance of a dataset.

COVAR({var1},{var2})Try this exampledata_y (array)The range representing the array or matrix of dependent data.data_x (array)The range representing the array or matrix of independent data. - CRITBINOM
Calculates the smallest value for which the cumulative binomial distribution is greater than or equal to a specified criteria.

CRITBINOM(100,0.005,0.8)Try this examplenum_trials (number)The number of independent trials.prob_success (number)The probability of success in any given trial. - DEVSQ
Calculates the sum of squares of deviations based on a sample.

DEVSQ(1,2,3,4,5,6,7,8,9,10)Try this examplevalue1 (number)The first value or range of the sample.value2 (number)Additional values or ranges to include in the sample. - EXPON_DIST
Returns the value of the exponential distribution function with a specified lambda at a specified value.

EXPON_DIST(4,0.5,FALSE)Try this examplex (number)The input to the exponential distribution function.lambda (number)The lambda to specify the exponential distribution function. - EXPONDIST
Returns the value of the exponential distribution function with a specified lambda at a specified value.

EXPONDIST(4,0.5,FALSE)Try this examplex (number)The input to the exponential distribution function.lambda (number)The lambda to specify the exponential distribution function. - FISHER
Returns the Fisher transformation of a specified value.

FISHER(0.962)Try this examplevalue (number)The value for which to calculate the Fisher transformation. - FISHERINV
Returns the inverse Fisher transformation of a specified value.

FISHERINV(0.962)Try this examplevalue (number)The value for which to calculate the inverse Fisher transformation. - FORECAST
Calculates the expected y-value for a specified x based on a linear regression of a dataset.

FORECAST(1,{var1},{var2})Try this examplex (number)The value on the x-axis to forecast.data_y (array)The range representing the array or matrix of dependent data. - GAMMA_DIST
The GAMMA_DIST function calculates the gamma distribution, a 2-parameter continuous probability distribution.

GAMMA_DIST(4.79, 1.234, 7, TRUE)Try this examplex (number)The input to the gamma probability distribution function. The value at which to evaluate the function.alpha (number)The first parameter of the distribution. - GAMMA_INV
The GAMMA_INV function returns the value of the inverse gamma cumulative distribution function for the specified probability, alpha, and beta parameters.

GAMMA_INV(0.65, 4, 2)Try this exampleprobability (number)The input to the inverse gamma distribution function.alpha (number)The shape of the gamma distribution. - GAMMADIST
The GAMMADIST function calculates the gamma distribution, a 2-parameter continuous probability distribution.

GAMMADIST(4.79, 1.234, 7, TRUE)Try this examplex (number)The input to the gamma probability distribution function. The value at which to evaluate the function.alpha (number)The first parameter of the distribution. - GAMMAINV
The GAMMAINV function returns the value of the inverse gamma cumulative distribution function for the specified probability, alpha, and beta parameters.

GAMMAINV(0.65, 4, 2)Try this exampleprobability (number)The input to the inverse gamma distribution function. Must be between 0 and 1.alpha (number)The shape of the gamma distribution. Must be positive. The sign of significance is ignored. - GAMMALN
Returns the the logarithm of a specified Gamma function, base e (Euler's number).

GAMMALN(4)Try this examplevalue (number)The input to the Gamma function. The natural logarithm of Gamma (value) will be returned. Value must be positive. - GEOMEAN
Calculates the geometric mean of a dataset.

GEOMEAN(1,2,3,4,5,6,7,8,9,10)Try this examplevalue1 (number)The first value or range of the population.value2 (number)Additional values or ranges to include in the population. - GROWTH
Given partial data about an exponential growth trend, fits an ideal exponential growth trend and/or predicts further values.

GROWTH({var1},{var2})Try this exampleknown_data_y (array)The array or range containing dependent (y) values that are already known, used to curve fit an ideal exponential growth curve.known_data_x (array)Optional - {1,2,3,...} with same length as known_data_y by default - The values of the independent variable(s) corresponding with known_data_y. - HARMEAN
Calculates the harmonic mean of a dataset.

HARMEAN(1,2,3,4,5,6,7,8,9,10)Try this examplevalue1 (number)The first value or range of the population.value2 (number)Additional values or ranges to include in the population. - HYPGEOM_DIST
Calculates the probability of drawing a certain number of successes in a certain number of tries given a population of a certain size containing a certain number of successes, without replacement of draws.

HYPGEOM_DIST(4,12,20,40)Try this examplenum_successes (number)The desired number of successes.num_draws (number)The number of permitted draws. - HYPGEOMDIST
Calculates the probability of drawing a certain number of successes in a certain number of tries given a population of a certain size containing a certain number of successes, without replacement of draws.

HYPGEOMDIST(4,12,20,40)Try this examplenum_successes (number)The desired number of successes.num_draws (number)The number of permitted draws. - INTERCEPT
Calculates the y-value at which the line resulting from linear regression of a dataset will intersect the y-axis (x=0).

INTERCEPT({var1},{var2})Try this exampledata_y (array)The range representing the array or matrix of dependent data.data_x (array)The range representing the array or matrix of independent data. - KURT
Calculates the kurtosis of a dataset, which describes the shape, and in particular the "peakedness" of that dataset.

KURT(1,2,3,4,5,6,7,8,9,10)Try this examplevalue1 (number)The first value or range of the dataset.value2 (number)Additional values or ranges to include in the dataset. - LARGE
Returns the nth largest element from a data set, where n is user-defined.

LARGE({var1},4)Try this exampledata (array)Array or range containing the dataset to consider.n (number)The rank from largest to smallest of the element to return. - LINEST
Given partial data about a linear trend, calculates various parameters about the ideal linear trend using the least-squares method.

LINEST({var1}, {var2})Try this exampleknown_data_y (array)The array or range containing dependent (y) values that are already known, used to curve fit an ideal linear trend.known_data_x (array)Optional - {1,2,3,...} with same length as known_data_y by default - The values of the independent variable(s) corresponding with known_data_y. - LOGEST
Given partial data about an exponential growth curve, calculates various parameters about the best fit ideal exponential growth curve.

LOGEST({var1},{var2})Try this exampleknown_data_y (array)The array or range containing dependent (y) values that are already known, used to curve fit an ideal exponential growth curve.known_data_x (array)Optional - {1,2,3,...} with same length as known_data_y by default - The values of the independent variable(s) corresponding with known_data_y. - LOGINV
Returns the value of the inverse log-normal cumulative distribution with given mean and standard deviation at a specified value.

LOGINV(0.4,4,6)Try this examplex (number)The input to the inverse log-normal cumulative distribution function.mean (number)The mean (mu) of the inverse log-normal cumulative distribution function. - LOGNORM_DIST
Returns the value of the log-normal cumulative distribution with given mean and standard deviation at a specified value.

LOGNORM_DIST(4,4,6)Try this examplex (number)The input to the log-normal cumulative distribution function.mean (number)The mean (mu) of the log-normal cumulative distribution function. - LOGNORMDIST
Returns the value of the log-normal cumulative distribution with given mean and standard deviation at a specified value.

LOGNORMDIST(4,4,6)Try this examplex (number)The input to the log-normal cumulative distribution function.mean (number)The mean (mu) of the log-normal cumulative distribution function. - MAX
Returns the maximum value in a numeric dataset.

MAX(1,2,3,4,5)Try this examplevalue1 (number)The first value or range to consider when calculating the maximum value.value2 (number)Optional: Additional values or ranges to consider when calculating the maximum value. - MAXA
Returns the maximum numeric value in a dataset.

MAXA(1,2,3,4,5)Try this examplevalue1 (number)The first value or range to consider when calculating the maximum value.value2 (number)Optional: Additional values or ranges to consider when calculating the maximum value. - MAXIFS
Returns the maximum value in a range of cells, filtered by a set of criteria.

MAXIFS({var1}, {var2}, 1, {var3}, "A")Try this examplerange (array)The range from which the maximum will be determined.criteria_range1 (array)The range over which to evaluate criterion1. - MEDIAN
Returns the median value in a numeric dataset.

MEDIAN(1,2,3,4,5)Try this examplevalue1 (number)The first value or range to consider when calculating the median value.value2 (number)Optional: Additional values or ranges to consider when calculating the median value. - MIN
Returns the minimum value in a numeric dataset.

MIN(1,2,3,4,5)Try this examplevalue1 (number)The first value or range to consider when calculating the minimum value.value2 (number)Optional: Additional values or ranges to consider when calculating the minimum value. - MINA
Returns the minimum numeric value in a dataset.

MINA(1,2,3,4,5)Try this examplevalue1 (number)The first value or range to consider when calculating the minimum value.value2 (number)Optional: Additional values or ranges to consider when calculating the minimum value. - MINIFS
Returns the minimum value in a range of cells, filtered by a set of criteria.

MINIFS({var1}, {var2}, 1, {var3}, "A")Try this examplerange (array)The range from which the minimum will be determined.criteria_range1 (array)The range over which to evaluate criterion1. - MODE
Returns the most commonly occurring value in a dataset.

MODE(1,2,3,4,5)Try this examplevalue1 (number)The first value or range to consider when calculating mode.value2 (number)Optional: Additional values or ranges to consider when calculating mode. - MODE_SNGL
Returns the most frequently occurring number in a numeric data set. For example, MODE_SNGL(1,2,4,4,5,5,5,6) returns 5.

MODE_SNGL(1,2,4,4,5,5,5,6)Try this examplenumber1 (number)A number.number2 (number)Optional: A number - NEGBINOM_DIST
Calculates the probability of drawing a certain number of failures before a certain number of successes given a probability of success in independent trials.

NEGBINOM_DIST(4,2,0.1)Try this examplenum_failures (number)The number of failures to model.num_successes (number)The number of successes to model. - NEGBINOMDIST
Calculates the probability of drawing a certain number of failures before a certain number of successes given a probability of success in independent trials.

NEGBINOMDIST(4,2,0.1)Try this examplenum_failures (number)The number of failures to model.num_successes (number)The number of successes to model. - NORM_DIST
Returns the value of the normal distribution function (or normal cumulative distribution function) for a specified value, mean, and standard deviation.

NORM_DIST(2.4,1,4,FALSE)Try this examplex (number)The input to the normal distribution function.mean (number)The mean (mu) of the normal distribution function. - NORM_S_DIST
Returns the value of the standard normal cumulative distribution function for a specified value.

NORM_S_DIST(2.4)Try this examplex (number)The input to the standard normal cumulative distribution function. - NORM_S_INV
Returns the value of the inverse standard normal distribution function for a specified value.

NORM_S_INV(0.75)Try this examplex (number)The input to the inverse standard normal distribution function. - NORMDIST
The NORMDIST function returns the value of the normal distribution function (or normal cumulative distribution function) for a specified value, mean, and standard deviation.

NORMDIST(2.4,1,4,FALSE)Try this examplex (number)The input to the normal distribution function.mean (number)The mean (mu) of the normal distribution function. - NORMINV
Returns the value of the inverse normal distribution function for a specified value, mean, and standard deviation.

NORMINV(.75,1,4)Try this examplex (number)The input to the normal distribution function.mean (number)The mean (mu) of the normal distribution function. - NORMSDIST
Returns the value of the standard normal cumulative distribution function for a specified value.

NORMSDIST(2.4)Try this examplex (number)The input to the standard normal cumulative distribution function. - NORMSINV
Returns the value of the inverse standard normal distribution function for a specified value.

NORMSINV(.75)Try this examplex (number)The input to the inverse standard normal distribution function. - PEARSON
Calculates r, the Pearson product-moment correlation coefficient of a dataset.

PEARSON({var1},{var2})Try this exampledata_y (array)The range representing the array or matrix of dependent data.data_x (array)The range representing the array or matrix of independent data. - PERCENTILE
Returns the value at a given percentile of a dataset.

PERCENTILE({var1},12)Try this exampledata (array)The array or range containing the dataset to consider.percentile (number)The percentile whose value within data will be calculated and returned. - PERCENTRANK
Returns the percentage rank (percentile) of a specified value in a dataset.

PERCENTRANK({var1},12)Try this exampledata (array)The array or range containing the dataset to consider.value (number)The value whose percentage rank will be determined. - PERMUT
Returns the number of ways to choose some number of objects from a pool of a given size of objects, considering order.

PERMUT(4,2)Try this examplen (number)The size of the pool of objects to choose from.k (number)The number of objects to choose. - POISSON
Returns the value of the Poisson distribution function (or Poisson cumulative distribution function) for a specified value and mean.

POISSON(2.4,1,FALSE)Try this examplex (number)The input to the Poisson distribution function.mean (number)The mean (mu) of the Poisson distribution function. - QUARTILE
Returns a value nearest to a specified quartile of a dataset.

QUARTILE({var1},3)Try this exampledata (array)The array or range containing the dataset to consider.quartile_number (number)Which quartile value to return. 0 - 0% mark, 1 - 25%, 2 - 50%, 3 - 75%, 4 - 100% - RANK
Returns the rank of a specified value in a dataset.

RANK(10,{var1})Try this examplevalue (number)The value whose rank will be determined.data (array)The array or range containing the dataset to consider. - RSQ
Calculates the square of r, the Pearson product-moment correlation coefficient of a dataset.

RSQ({var1},{var2})Try this exampledata_y (array)The range representing the array or matrix of dependent data.data_x (array)The range representing the array or matrix of independent data. - SKEW
Calculates the skewness of a dataset, which describes the symmetry of that dataset about the mean.

SKEW(1,2,3,4,5,6,7,8,9,10)Try this examplevalue1 (number)The first value or range of the dataset.value2 (number)Additional values or ranges to include in the dataset. - SLOPE
Calculates the slope of the line resulting from linear regression of a dataset.

SLOPE({var1},{var2})Try this exampledata_y (array)The range representing the array or matrix of dependent data.data_x (array)The range representing the array or matrix of independent data. - SMALL
Returns the nth smallest element from a data set, where n is user-defined.

SMALL({var1},4)Try this exampledata (array)The array or range containing the dataset to consider.n (number)The rank from smallest to largest of the element to return. - STANDARDIZE
Calculates the normalized equivalent of a random variable given mean and standard deviation of the distribution.

STANDARDIZE(96,80,6.7)Try this examplevalue (number)The value of the random variable to normalize.mean (number)The mean of the distribution. - STDEV
The STDEV function calculates the standard deviation based on a sample.

STDEV(1,2,3,4,5,6,7,8,9,10)Try this examplevalue1 (number)The first value or range of the sample.value2 (number)Optional: Additional values or ranges to include in the sample. - STDEV_P
Calculates the standard deviation for a sample set of data. STDEV_P calculates standard deviation using the "n" method, ignoring logical values and text.

STDEV_P({var1})Try this examplenumber1 (mixed)First number or reference in the sample.value1 (number)The first value or range of the population. - STDEV_S
Calculates the standard deviation for a sample set of data. STDEV_S replaces the older STDEV function, with the same behavior.

STDEV_S({var1})Try this examplenumber1 (mixed)First number or reference in the sample.number2 (mixed)Optional: Second number or reference. - STDEVA
Calculates the standard deviation based on a sample, setting text to the value `0`.

STDEVA(1,2,3,4,5,6,7,8,9,10)Try this examplevalue1 (number)The first value or range of the sample.value2 (number)Additional values or ranges to include in the sample. - STDEVP
Calculates the standard deviation based on an entire population.

STDEVP(1,2,3,4,5,6,7,8,9,10)Try this examplevalue1 (number)The first value or range of the population.value2 (number)Additional values or ranges to include in the population. - STDEVPA
Calculates the standard deviation based on an entire population, setting text to the value `0`.

STDEVPA(1,2,3,4,5,6,7,8,9,10)Try this examplevalue1 (number)The first value or range of the population.value2 (number)Additional values or ranges to include in the population. - STEYX
Calculates the standard error of the predicted y-value for each x in the regression of a dataset.

STEYX({var1},{var2})Try this exampledata_y (array)The range representing the array or matrix of dependent data.data_x (array)The range representing the array or matrix of independent data. - T_DIST
Returns the Percentage Points (probability) for the Student t-distribution

T_DIST(1.96, 60, FALSE)Try this examplex (number)The x value to evaluate the distribution at.degrees_freedom (number)The degrees of freedom. - T_INV
Calculates the negative inverse of the one-tailed TDIST function.

T_INV(0.35, 1)Try this exampleprobability (number)The probability associated with the t-distribution.degrees_freedom (number)The number of degrees of freedom. - TDIST
Calculates the probability for Student's t-distribution with a given input (x).

TDIST(0.5, 1, 2)Try this examplex (number)The input to the t-distribution function.degrees_freedom (number)The number of degrees of freedom. - TINV
Calculates the inverse of the two-tailed TDIST function.

TINV(0.35, 1)Try this exampleprobability (number)The probability associated with the two-tailed t-distribution.degrees_freedom (number)The number of degrees of freedom. - TREND
Given partial data about a linear trend, fits an ideal linear trend using the least squares method and/or predicts further values.

TREND({var1},{var2})Try this exampleknown_data_y (array)The array or range containing dependent (y) values that are already known, used to curve fit an ideal linear trend.known_data_x (array) - TRIMMEAN
Calculates the mean of a dataset excluding some proportion of data from the high and low ends of the dataset.

TRIMMEAN({1,1,2,3,5,8,13,21,34,55},0.05)Try this exampledata (array)Array or range containing the dataset to consider.exclude_proportion (number)The proportion of the dataset to exclude, from the extremities of the set. - VAR
Calculates the variance based on a sample.

VAR(1,2,3,4,5,6,7,8,9,10)Try this examplevalue1 (number)The first value or range of the sample.value2 (number)Additional values or ranges to include in the sample. - VAR_P
Returns the variance in an entire population. If data represents a sample of the population, use the VAR_S function.

VAR_P({var1})Try this examplenumber1 (mixed)First number or reference.number2 (mixed)Optional: Second number or reference. - VAR_S
Returns the variance of a sample. If data represents the entire population, use the VAR_P function. VAR_S ignores text values and logicals in references.

VAR_S({var1})Try this examplenumber1 (mixed)First number or reference.number2 (mixed)Optional: Second number or reference. - VARA
Calculates the variance based on a sample, setting text to the value `0`.

VARA(1,2,3,4,5,6,7,8,9,10)Try this examplevalue1 (number)The first value or range of the sample.value2 (number)Additional values or ranges to include in the sample. - VARP
Calculates the variance based on an entire population.

VARP(1,2,3,4,5,6,7,8,9,10)Try this examplevalue1 (number)The first value or range of the population.value2 (number)Additional values or ranges to include in the population. - VARPA
Calculates the variance based on an entire population, setting text to the value `0`.

VARPA(1,2,3,4,5,6,7,8,9,10)Try this examplevalue1 (number)The first value or range of the population.value2 (number)Additional values or ranges to include in the population. - WEIBULL
Returns the value of the Weibull distribution function (or Weibull cumulative distribution function) for a specified shape and scale.

WEIBULL(2.4, 2, 3, TRUE)Try this examplex (number)The input to the Weibull distribution function.shape (number)The shape parameter of the Weibull distribution function. - Z_TEST
Returns the one-tailed probability-value of a z-test

Z_TEST([1,2,3,4,5,6],5.5,1.2)Try this exampledata (array)The array or range containing the dataset to consider.value (number)The test statistic to use in the Z-test. - ZTEST
Returns the one-tailed P-value of a Z-test with standard distribution.

ZTEST([1,2,3,4,5,6],5.5,1.2)Try this exampledata (array)The array or range containing the dataset to consider.value (number)The test statistic to use in the Z-test.