## List of Excel Statistical Functions with Syntax and Arguments

This reference table lists all officially documented Statistical Functions. It is based on the Excel Help documentation and linked in each row when clicking on the function name. Export the table and/or display additional columns via the symbols to the top right of the table. Please report errors or inconsistencies via the comment function at the bottom of this page. Also check the main table with List of all Excel Functions.

#Excel FunctionDescriptionSyntax with ArgumentsSyntax onlyArguments only
1AVEDEVReturns the average of the absolute deviations of data points from their mean=AVEDEV(number1number1 [required]: Number for which you want the average of the absolute deviations., [number2], ...)=AVEDEV(number1, [number2], ...)
• number1 (required): Number for which you want the average of the absolute deviations.
2AVERAGE returns the average of those numbers=AVERAGE(number1number1 [required]: The first number, cell reference, or range for which you want the average., [number2number2 [optional]: Number for which you want the average of the absolute deviations.], ...)=AVERAGE(number1, [number2], ...)
• number1 (required): The first number, cell reference, or range for which you want the average.
• number2 (optional): Number for which you want the average of the absolute deviations.
3AVERAGEACalculates the average (arithmetic mean) of the values in the list of arguments=AVERAGEA(value1value1 [required]: Calculates the average of the values in the list of arguments, [value2value2 [optional]: Optional additional value], ...)=AVERAGEA(value1, [value2], ...)
• value1 (required): Calculates the average of the values in the list of arguments
• value2 (optional): Optional additional value
4AVERAGEIFReturns the average (arithmetic mean) of all the cells in a range that meet a given criteria=AVERAGEIF(rangerange [required]: One or more cells to average, including numbers or names, arrays, or references that contain numbers., criteriacriteria [required]: The criteria in the form of a number, expression, cell reference, or text that defines which cells are averaged., [average_rangeaverage_range [optional]: The actual set of cells to average.])=AVERAGEIF(range, criteria, [average_range])
• range (required): One or more cells to average, including numbers or names, arrays, or references that contain numbers.
• criteria (required): The criteria in the form of a number, expression, cell reference, or text that defines which cells are averaged.
• average_range (optional): The actual set of cells to average.
5AVERAGEIFSReturns the average (arithmetic mean) of all cells that meet multiple criteria=AVERAGEIFS(average_rangeaverage_range [required]: One or more cells to average, including numbers or names, arrays, or references that contain numbers., criteria_range1criteria_range1 [required]: Range in which to evaluate the associated crite. Additional ranges are optional, criteria1criteria1 [required]: Criteria in the form of a number, expression, cell reference, or text that define which cells will be averaged., [criteria_range2, criteria2criteria_range2, criteria2 [optional]: Additional optional range/criteria], ...)=AVERAGEIFS(average_range, criteria_range1, criteria1, [criteria_range2, criteria2], ...)
• average_range (required): One or more cells to average, including numbers or names, arrays, or references that contain numbers.
• criteria_range1 (required): Range in which to evaluate the associated crite. Additional ranges are optional
• criteria1 (required): Criteria in the form of a number, expression, cell reference, or text that define which cells will be averaged.
• criteria_range2, criteria2 (optional): Additional optional range/criteria
6BETA.DISTReturns the beta distribution=BETA.DIST(xx [required]: The value between A and B at which to evaluate the function

, alphaalpha [required]: A parameter of the distribution., betabeta [required]: A parameter of the distribution., cumulativecumulative [required]: A logical value that determines the form of the function., [aa [optional]: A lower bound to the interval of x.], [bb [optional]: An upper bound to the interval of x.])
=BETA.DIST(x, alpha, beta, cumulative, [a], [b])
• x (required): The value between A and B at which to evaluate the function

• alpha (required): A parameter of the distribution.
• beta (required): A parameter of the distribution.
• cumulative (required): A logical value that determines the form of the function.
• a (optional): A lower bound to the interval of x.
• b (optional): An upper bound to the interval of x.
7BETA.INVReturns the inverse of the beta cumulative probability density function (BETA=BETA.INV(probabilityprobability [required]: A probability associated with the beta distribution., alphaalpha [required]: A parameter of the distribution., betabeta [required]: A parameter the distribution., [aa [optional]: A lower bound to the interval of x.], [bb [optional]: An upper bound to the interval of x.])=BETA.INV(probability, alpha, beta, [a], [b])
• probability (required): A probability associated with the beta distribution.
• alpha (required): A parameter of the distribution.
• beta (required): A parameter the distribution.
• a (optional): A lower bound to the interval of x.
• b (optional): An upper bound to the interval of x.
8BINOM.DISTReturns the individual term binomial distribution probability=BINOM.DIST(number_snumber_s [required]: The number of successes in trials., trialstrials [required]: The number of independent trials., probability_sprobability_s [required]: The probability of success on each trial., cumulativecumulative [required]: A logical value that determines the form of the function.)=BINOM.DIST(number_s, trials, probability_s, cumulative)
• number_s (required): The number of successes in trials.
• trials (required): The number of independent trials.
• probability_s (required): The probability of success on each trial.
• cumulative (required): A logical value that determines the form of the function.
9BINOM.DIST.RANGEReturns the probability of a trial result using a binomial distribution=BINOM.DIST.RANGE(trialstrials [required]: The number of independent trials., probability_sprobability_s [required]: The probability of success in each trial., number_snumber_s [required]: The number of successes in trials., [number_s2number_s2 [optional]: If provided, returns the probability that the number of successful trials will fall between Number_s and number_s2.])=BINOM.DIST.RANGE(trials, probability_s, number_s, [number_s2])
• trials (required): The number of independent trials.
• probability_s (required): The probability of success in each trial.
• number_s (required): The number of successes in trials.
• number_s2 (optional): If provided, returns the probability that the number of successful trials will fall between Number_s and number_s2.
10BINOM.INVReturns the smallest value for which the cumulative binomial distribution is greater than or equal to a criterion value=BINOM.INV(trialstrials [required]: The number of Bernoulli trials., probability_sprobability_s [required]: The probability of a success on each trial., alphaalpha [required]: The criterion value.)=BINOM.INV(trials, probability_s, alpha)
• trials (required): The number of Bernoulli trials.
• probability_s (required): The probability of a success on each trial.
• alpha (required): The criterion value.
11CHISQ.DISTReturns the chi-squared distribution=CHISQ.DIST(xx [required]: The value at which you want to evaluate the distribution., deg_freedomdeg_freedom [required]: The number of degrees of freedom., cumulativecumulative [required]: A logical value that determines the form of the function.)=CHISQ.DIST(x, deg_freedom, cumulative)
• x (required): The value at which you want to evaluate the distribution.
• deg_freedom (required): The number of degrees of freedom.
• cumulative (required): A logical value that determines the form of the function.
12CHISQ.DIST.RTReturns the right-tailed probability of the chi-squared distribution=CHISQ.DIST.RT(xx [required]: The value at which you want to evaluate the distribution., deg_freedomdeg_freedom [required]: The number of degrees of freedom.)=CHISQ.DIST.RT(x, deg_freedom)
• x (required): The value at which you want to evaluate the distribution.
• deg_freedom (required): The number of degrees of freedom.
13CHISQ.INVReturns the inverse of the left-tailed probability of the chi-squared distribution=CHISQ.INV(probabilityprobability [required]: A probability associated with the chi-squared distribution., deg_freedomdeg_freedom [required]: The number of degrees of freedom.)=CHISQ.INV(probability, deg_freedom)
• probability (required): A probability associated with the chi-squared distribution.
• deg_freedom (required): The number of degrees of freedom.
14CHISQ.INV.RTReturns the inverse of the right-tailed probability of the chi-squared distribution=CHISQ.INV.RT(probabilityprobability [required]: A probability associated with the chi-squared distribution., deg_freedomdeg_freedom [required]: The number of degrees of freedom.)=CHISQ.INV.RT(probability, deg_freedom)
• probability (required): A probability associated with the chi-squared distribution.
• deg_freedom (required): The number of degrees of freedom.
15CHISQ.TESTReturns the test for independence=CHISQ.TEST(actual_rangeactual_range [required]: The range of data that contains observations to test against expected values., expected_rangeexpected_range [required]: The range of data that contains the ratio of the product of row totals and column totals to the grand total.)=CHISQ.TEST(actual_range, expected_range)
• actual_range (required): The range of data that contains observations to test against expected values.
• expected_range (required): The range of data that contains the ratio of the product of row totals and column totals to the grand total.
16CONFIDENCE.NORMReturns the confidence interval for a population mean, using a normal distribution=CONFIDENCE.NORM(alphaalpha [required]: The significance level used to compute the confidence level., standard_devstandard_dev [required]: The population standard deviation for the data range and is assumed to be known., sizesize [required]: The sample size.)=CONFIDENCE.NORM(alpha, standard_dev, size)
• alpha (required): The significance level used to compute the confidence level.
• standard_dev (required): The population standard deviation for the data range and is assumed to be known.
• size (required): The sample size.
17CONFIDENCE.TReturns confidence interval for a population mean, using t distribution=CONFIDENCE.T(alphaalpha [required]: The significance level used to compute the confidence level., standard_devstandard_dev [required]: The population standard deviation for the data range and is assumed to be known., sizesize [required]: The sample size.)=CONFIDENCE.T(alpha, standard_dev, size)
• alpha (required): The significance level used to compute the confidence level.
• standard_dev (required): The population standard deviation for the data range and is assumed to be known.
• size (required): The sample size.
18CORRELReturns the correlation coefficient of two cell ranges=CORREL(array1array1 [required]: A range of cell values., array2array2 [required]: A second range of cell values.)=CORREL(array1, array2)
• array1 (required): A range of cell values.
• array2 (required): A second range of cell values.
19COUNTCount the number of cells that contain numbers=COUNT(value1value1 [required]: The first item, cell reference, or range within which you want to count numbers., [value2value2 [optional]: Additional items to count.], ...)=COUNT(value1, [value2], ...)
• value1 (required): The first item, cell reference, or range within which you want to count numbers.
• value2 (optional): Additional items to count.
20COUNTACount the number of cells that are not empty in a range=COUNTA(value1value1 [required]: The first item, cell reference, or range within which you want to count numbers., [value2value2 [optional]: Additional items to count.], ...)=COUNTA(value1, [value2], ...)
• value1 (required): The first item, cell reference, or range within which you want to count numbers.
• value2 (optional): Additional items to count.
21COUNTBLANKCounts the number of empty cells in a range of cells=COUNTBLANK(rangerange [required]: The range from which you want to count the blank cells.)=COUNTBLANK(range)
• range (required): The range from which you want to count the blank cells.
22COUNTIFCount the number of cells that meet a citeria=COUNTIF(rangerange [required]: The range from which you want to count the blank cells., criteriacriteria [required]: The criteria in the form of a number, expression, cell reference, or text.)=COUNTIF(range, criteria)
• range (required): The range from which you want to count the blank cells.
• criteria (required): The criteria in the form of a number, expression, cell reference, or text.
23COUNTIFSApplies criteria to cells across multiple ranges and counts the number of times all criteria are met=COUNTIFS(criteria_range1criteria_range1 [required]: The first range in which to evaluate the associated criteria., criteria1criteria1 [required]: The criteria in the form of a number, expression, cell reference, or text., [criteria_range2, criteria2criteria_range2, criteria2 [optional]: Additional ranges and their associated criteria], ...)=COUNTIFS(criteria_range1, criteria1, [criteria_range2, criteria2], ...)
• criteria_range1 (required): The first range in which to evaluate the associated criteria.
• criteria1 (required): The criteria in the form of a number, expression, cell reference, or text.
• criteria_range2, criteria2 (optional): Additional ranges and their associated criteria
24COVARIANCE.PReturns population covariance, the average of the products of deviations for each data point pair in two data sets=COVARIANCE.P(array1array1 [required]: The first cell range of integers., array2array2 [required]: The second cell range of integers.)=COVARIANCE.P(array1, array2)
• array1 (required): The first cell range of integers.
• array2 (required): The second cell range of integers.
25COVARIANCE.SReturns the sample covariance, the average of the products of deviations for each data point pair in two data sets=COVARIANCE.S(array1array1 [required]: The first cell range of integers., array2array2 [required]: The second cell range of integers.)=COVARIANCE.S(array1, array2)
• array1 (required): The first cell range of integers.
• array2 (required): The second cell range of integers.
26DEVSQReturns the sum of squares of deviations of data points from their sample mean=DEVSQ(number1number1 [required]: Number for which you want to calculate the sum of squared deviations, [number2number2 [optional]: Additional numbers], ...)=DEVSQ(number1, [number2], ...)
• number1 (required): Number for which you want to calculate the sum of squared deviations
27EXPON.DISTReturns the exponential distribution=EXPON.DIST(xx [required]: The value of the function., lambdalambda [required]: The parameter value., cumulativecumulative [required]: A logical value that indicates which form of the exponential function to provide.)=EXPON.DIST(x, lambda, cumulative)
• x (required): The value of the function.
• lambda (required): The parameter value.
• cumulative (required): A logical value that indicates which form of the exponential function to provide.
28F.DISTReturns the F probability distribution (degree of diversity) for two data sets=F.DIST(xx [required]: The value at which to evaluate the function., deg_freedom1deg_freedom1 [required]: The numerator degrees of freedom., deg_freedom2deg_freedom2 [required]: The denominator degrees of freedom., cumulativecumulative [required]: A logical value that determines the form of the function.)=F.DIST(x, deg_freedom1, deg_freedom2, cumulative)
• x (required): The value at which to evaluate the function.
• deg_freedom1 (required): The numerator degrees of freedom.
• deg_freedom2 (required): The denominator degrees of freedom.
• cumulative (required): A logical value that determines the form of the function.
29F.DIST.RTReturns the (right-tailed) F probability distribution (degree of diversity) for two data sets=F.DIST.RT(xx [required]: The value at which to evaluate the function., deg_freedom1deg_freedom1 [required]: The numerator degrees of freedom., deg_freedom2deg_freedom2 [required]: The denominator degrees of freedom.)=F.DIST.RT(x, deg_freedom1, deg_freedom2)
• x (required): The value at which to evaluate the function.
• deg_freedom1 (required): The numerator degrees of freedom.
• deg_freedom2 (required): The denominator degrees of freedom.
30F.INVReturns the inverse of the F probability distribution=F.INV(probabilityprobability [required]: A probability associated with the F cumulative distribution., deg_freedom1deg_freedom1 [required]: The numerator degrees of freedom., deg_freedom2deg_freedom2 [required]: The denominator degrees of freedom.)=F.INV(probability, deg_freedom1, deg_freedom2)
• probability (required): A probability associated with the F cumulative distribution.
• deg_freedom1 (required): The numerator degrees of freedom.
• deg_freedom2 (required): The denominator degrees of freedom.
31F.INV.RTReturns the inverse of the (right-tailed) F probability distribution=F.INV.RT(probabilityprobability [required]: A probability associated with the F cumulative distribution., deg_freedom1deg_freedom1 [required]: The numerator degrees of freedom., deg_freedom2deg_freedom2 [required]: The denominator degrees of freedom.)=F.INV.RT(probability, deg_freedom1, deg_freedom2)
• probability (required): A probability associated with the F cumulative distribution.
• deg_freedom1 (required): The numerator degrees of freedom.
• deg_freedom2 (required): The denominator degrees of freedom.
32F.TESTReturns the result of an F-test, the two-tailed probability that the variances in array1 and array2 are not significantly different=F.TEST(array1array1 [required]: The first array or range of data., array2array2 [required]: The second array or range of data.)=F.TEST(array1, array2)
• array1 (required): The first array or range of data.
• array2 (required): The second array or range of data.
33FINVReturns the inverse of the (right-tailed) F probability distribution=FINV(probabilityprobability [required]: A probability associated with the F cumulative distribution., deg_freedom1deg_freedom1 [required]: The numerator degrees of freedom., deg_freedom2deg_freedom2 [required]: The denominator degrees of freedom.)=FINV(probability, deg_freedom1, deg_freedom2)
• probability (required): A probability associated with the F cumulative distribution.
• deg_freedom1 (required): The numerator degrees of freedom.
• deg_freedom2 (required): The denominator degrees of freedom.
34FISHERReturns the Fisher transformation at x=FISHER(xx [required]: A numeric value for which you want the transformation.)=FISHER(x)
• x (required): A numeric value for which you want the transformation.
35FISHERINVReturns the inverse of the Fisher transformation=FISHERINV(yy [required]: The value for which you want to perform the inverse of the transformation.)=FISHERINV(y)
• y (required): The value for which you want to perform the inverse of the transformation.
36FORECASTCalculate, or predict, a future value by using existing values=FORECAST(xx [required]: The data point for which you want to predict a value., known_y'sknown_y's [required]: The dependent array or range of data., known_x'sknown_x's [required]: The independent array or range of data.)=FORECAST(x, known_y's, known_x's)
• x (required): The data point for which you want to predict a value.
• known_y's (required): The dependent array or range of data.
• known_x's (required): The independent array or range of data.
37FORECAST.ETSCalculates or predicts a future value based on existing (historical) values by using the AAA version of the Exponential Smoothing (ETS) algorithm=FORECAST.ETS(target_datetarget_date [required]: The data point for which you want to predict a value., valuesvalues [required]: Values are the historical values, for which you want to forecast the next points., timelinetimeline [required]: The independent array or range of numeric data. , [seasonalityseasonality [optional]: Numeric value indicating seasonality.], [data_completiondata_completion [optional]: Numeric value indicating algorithm to account for for missing points.], [aggregationaggregation [optional]: Numeric value indicating which method will be used to aggregate several values with the same time stamp])=FORECAST.ETS(target_date, values, timeline, [seasonality], [data_completion], [aggregation])
• target_date (required): The data point for which you want to predict a value.
• values (required): Values are the historical values, for which you want to forecast the next points.
• timeline (required): The independent array or range of numeric data.
• seasonality (optional): Numeric value indicating seasonality.
• data_completion (optional): Numeric value indicating algorithm to account for for missing points.
• aggregation (optional): Numeric value indicating which method will be used to aggregate several values with the same time stamp
38FORECAST.ETS.CONFINTReturns a confidence interval for the forecast value at the specified target date. =FORECAST.ETS.CONFINT(target_datetarget_date [required]: The data point for which you want to predict a value., valuesvalues [required]: Values are the historical values, for which you want to forecast the next points., timelinetimeline [required]: The independent array or range of numeric data. , [confidence_levelconfidence_level [optional]: A numerical value between 0 and 1 (exclusive), indicating a confidence level for the calculated confidence interval.], [seasonalityseasonality [optional]: Numeric value indicating seasonality.], [data_completiondata_completion [optional]: Numeric value indicating algorithm to account for for missing points.], [aggregationaggregation [optional]: Numeric value indicating which method will be used to aggregate several values with the same time stamp])=FORECAST.ETS.CONFINT(target_date, values, timeline, [confidence_level], [seasonality], [data_completion], [aggregation])
• target_date (required): The data point for which you want to predict a value.
• values (required): Values are the historical values, for which you want to forecast the next points.
• timeline (required): The independent array or range of numeric data.
• seasonality (optional): Numeric value indicating seasonality.
• data_completion (optional): Numeric value indicating algorithm to account for for missing points.
• aggregation (optional): Numeric value indicating which method will be used to aggregate several values with the same time stamp
• confidence_level (optional): A numerical value between 0 and 1 (exclusive), indicating a confidence level for the calculated confidence interval.
39FORECAST.ETS.SEASONALITYReturns the length of the repetitive pattern Excel detects for the specified time series=FORECAST.ETS.SEASONALITY(valuesvalues [required]: Values are the historical values, for which you want to forecast the next points., timelinetimeline [required]: The independent array or range of numeric data. , [data_completiondata_completion [optional]: Numeric value indicating algorithm to account for for missing points.], [aggregationaggregation [optional]: Numeric value indicating which method will be used to aggregate several values with the same time stamp])=FORECAST.ETS.SEASONALITY(values, timeline, [data_completion], [aggregation])
• values (required): Values are the historical values, for which you want to forecast the next points.
• timeline (required): The independent array or range of numeric data.
• data_completion (optional): Numeric value indicating algorithm to account for for missing points.
• aggregation (optional): Numeric value indicating which method will be used to aggregate several values with the same time stamp
40FORECAST.ETS.STATReturns a statistical value as a result of time series forecasting=FORECAST.ETS.STAT(valuesvalues [required]: Values are the historical values, for which you want to forecast the next points., timelinetimeline [required]: The independent array or range of numeric data. , statistic_typestatistic_type [required]: A numeric value between 1 and 8, indicating which statistic will be returned for the calculated forecast., [seasonalityseasonality [optional]: Numeric value indicating seasonality.], [data_completiondata_completion [optional]: Numeric value indicating algorithm to account for for missing points.], [aggregationaggregation [optional]: Numeric value indicating which method will be used to aggregate several values with the same time stamp])=FORECAST.ETS.STAT(values, timeline, statistic_type, [seasonality], [data_completion], [aggregation])
• values (required): Values are the historical values, for which you want to forecast the next points.
• timeline (required): The independent array or range of numeric data.
• statistic_type (required): A numeric value between 1 and 8, indicating which statistic will be returned for the calculated forecast.
• seasonality (optional): Numeric value indicating seasonality.
• data_completion (optional): Numeric value indicating algorithm to account for for missing points.
• aggregation (optional): Numeric value indicating which method will be used to aggregate several values with the same time stamp
41FORECAST.LINEARReturns a statistical value as a result of time series forecasting=FORECAST.LINEAR(xx [required]: The data point for which you want to predict a value., known_y'sknown_y's [required]: The dependent array or range of data., known_x'sknown_x's [required]: The independent array or range of data.)=FORECAST.LINEAR(x, known_y's, known_x's)
• x (required): The data point for which you want to predict a value.
• known_y's (required): The dependent array or range of data.
• known_x's (required): The independent array or range of data.
42FREQUENCYCalculates how often values occur within a range of values, and then returns a vertical array of numbers=FREQUENCY(data_arraydata_array [required]: An array of or reference to a set of values for which you want to count frequencies., bins_arraybins_array [required]: An array of or reference to intervals into which you want to group the values in data_array.)=FREQUENCY(data_array, bins_array)
• data_array (required): An array of or reference to a set of values for which you want to count frequencies.
• bins_array (required): An array of or reference to intervals into which you want to group the values in data_array.
43GAMMAReturn the gamma function value=GAMMA(numbernumber [required]: Returns a number.)=GAMMA(number)
• number (required): Returns a number.
44GAMMA.DISTReturns the gamma distribution=GAMMA.DIST(xx [required]: The value at which you want to evaluate the distribution., alphaalpha [required]: A parameter to the distribution., betabeta [required]: A parameter to the distribution., cumulativecumulative [required]: A logical value that determines the form of the function.)=GAMMA.DIST(x, alpha, beta, cumulative)
• x (required): The value at which you want to evaluate the distribution.
• alpha (required): A parameter to the distribution.
• beta (required): A parameter to the distribution.
• cumulative (required): A logical value that determines the form of the function.
45GAMMA.INVReturns the inverse of the gamma cumulative distribution=GAMMA.INV(probabilityprobability [required]: The probability associated with the gamma distribution., alphaalpha [required]: A parameter to the distribution., betabeta [required]: A parameter to the distribution.)=GAMMA.INV(probability, alpha, beta)
• probability (required): The probability associated with the gamma distribution.
• alpha (required): A parameter to the distribution.
• beta (required): A parameter to the distribution.
46GAMMALNReturns the natural logarithm of the gamma function=GAMMALN(xx [required]: The value for which you want to calculate GAMMALN.)=GAMMALN(x)
• x (required): The value for which you want to calculate GAMMALN.
47GAMMALN.PRECISEReturns the natural logarithm of the gamma function=GAMMALN.PRECISE(xx [required]: The value for which you want to calculate GAMMALN.)=GAMMALN.PRECISE(x)
• x (required): The value for which you want to calculate GAMMALN.
48GAUSSCalculates the probability that a member of a standard normal population will fall between the mean and z standard deviations from the mean=GAUSS(zz [required]: Returns a number.)=GAUSS(z)
• z (required): Returns a number.
49GEOMEANReturns the geometric mean of an array or range of positive data=GEOMEAN(number1number1 [required]: The first number arguments for which you want to calculate the mean., [number2number2 [optional]: Additional optional numbers], ...)=GEOMEAN(number1, [number2], ...)
• number1 (required): The first number arguments for which you want to calculate the mean.
• number2 (optional): Additional optional numbers
50GROWTHCalculates predicted exponential growth by using existing data=GROWTH(known_y'sknown_y's [required]: The set of y-values you already know in the relationship y = b*m^x., [known_x'sknown_x's [optional]: An optional set of x-values that you may already know in the relationship y = b*m^x.], [new_x'snew_x's [optional]: Are new x-values for which you want GROWTH to return corresponding y-values.], [constconst [optional]: A logical value specifying whether to force the constant b to equal 1.])=GROWTH(known_y's, [known_x's], [new_x's], [const])
• known_y's (required): The set of y-values you already know in the relationship y = b*m^x.
• known_x's (optional): An optional set of x-values that you may already know in the relationship y = b*m^x.
• new_x's (optional): Are new x-values for which you want GROWTH to return corresponding y-values.
• const (optional): A logical value specifying whether to force the constant b to equal 1.
51HARMEANReturns the harmonic mean of a data set=HARMEAN(number1number1 [required]: Number arguments for which you want to calculate the mean. , [number2number2 [optional]: Additional optional number arguments. ], ...)=HARMEAN(number1, [number2], ...)
• number1 (required): Number arguments for which you want to calculate the mean.
• number2 (optional): Additional optional number arguments.
52HYPGEOM.DISTReturns the hypergeometric distribution/ probability of a given number of sample successes, given the sample size, population successes, and population size. =HYPGEOM.DIST(sample_ssample_s [required]: The number of successes in the sample., number_samplenumber_sample [required]: The size of the sample., population_spopulation_s [required]: The number of successes in the population., number_popnumber_pop [required]: The population size., cumulativecumulative [required]: A logical value that determines the form of the function.)=HYPGEOM.DIST(sample_s, number_sample, population_s, number_pop, cumulative)
• sample_s (required): The number of successes in the sample.
• number_sample (required): The size of the sample.
• population_s (required): The number of successes in the population.
• number_pop (required): The population size.
• cumulative (required): A logical value that determines the form of the function.
53INTERCEPTCalculates the point at which a line will intersect the y-axis by using existing x-values and y-values=INTERCEPT(known_y'sknown_y's [required]: The dependent set of observations or data., known_x'sknown_x's [required]: The independent set of observations or data.)=INTERCEPT(known_y's, known_x's)
• known_y's (required): The dependent set of observations or data.
• known_x's (required): The independent set of observations or data.
54KURTReturns the kurtosis of a data set=KURT(number1number1 [required]: Number for which you want to calculate kurtosis., [number2number2 [optional]: Additional optional numbers.], ...)=KURT(number1, [number2], ...)
• number1 (required): Number for which you want to calculate kurtosis.
• number2 (optional): Additional optional numbers.
55LARGEReturns the k-th largest value in a data set=LARGE(arrayarray [required]: The array or range of data for which you want to determine the k-th largest value., kk [required]: The position (from the largest) in the array or cell range of data to return.)=LARGE(array, k)
• array (required): The array or range of data for which you want to determine the k-th largest value.
• k (required): The position (from the largest) in the array or cell range of data to return.
56LINESTCalculate the statistics for other types of models that are linear in the unknown parameters, including polynomial, logarithmic, exponential, and power series=LINEST(known_y'sknown_y's [required]: The et of y-values that you already know in the relationship y = mx + b., [known_x'sknown_x's [optional]: A set of x-values that you may already know in the relationship y = mx + b.], [constconst [optional]: A logical value specifying whether to force the constant b to equal 0.], [statsstats [optional]: A logical value specifying whether to return additional regression statistics.])=LINEST(known_y's, [known_x's], [const], [stats])
• known_y's (required): The et of y-values that you already know in the relationship y = mx + b.
• known_x's (optional): A set of x-values that you may already know in the relationship y = mx + b.
• const (optional): A logical value specifying whether to force the constant b to equal 0.
• stats (optional): A logical value specifying whether to return additional regression statistics.
57LOGESTCalculates an exponential curve that fits your data and returns an array of values that describes the curve. =LOGEST(known_y'sknown_y's [required]: The set of y-values you already know in the relationship y = b*m^x., [known_x'sknown_x's [optional]: An optional set of x-values that you may already know in the relationship y = b*m^x.], [constconst [optional]: A logical value specifying whether to force the constant b to equal 1.], [statsstats [optional]: A logical value specifying whether to return additional regression statistics.])=LOGEST(known_y's, [known_x's], [const], [stats])
• known_y's (required): The set of y-values you already know in the relationship y = b*m^x.
• known_x's (optional): An optional set of x-values that you may already know in the relationship y = b*m^x.
• const (optional): A logical value specifying whether to force the constant b to equal 1.
• stats (optional): A logical value specifying whether to return additional regression statistics.
58LOGNORM.DISTReturns the lognormal distribution of x, where ln(x) is normally distributed with parameters Mean and Standard_dev=LOGNORM.DIST(xx [required]: The value at which to evaluate the function., meanmean [required]: The mean of ln(x)., standard_devstandard_dev [required]: The standard deviation of ln(x)., cumulativecumulative [required]: A logical value that determines the form of the function.)=LOGNORM.DIST(x, mean, standard_dev, cumulative)
• x (required): The value at which to evaluate the function.
• mean (required): The mean of ln(x).
• standard_dev (required): The standard deviation of ln(x).
• cumulative (required): A logical value that determines the form of the function.
59LOGNORM.INVReturns the inverse of the lognormal cumulative distribution function of x, where ln(x) is normally distributed with parameters Mean and Standard_dev=LOGNORM.INV(probabilityprobability [required]: A probability associated with the lognormal distribution., meanmean [required]: The mean of ln(x)., standard_devstandard_dev [required]: The standard deviation of ln(x).)=LOGNORM.INV(probability, mean, standard_dev)
• probability (required): A probability associated with the lognormal distribution.
• mean (required): The mean of ln(x).
• standard_dev (required): The standard deviation of ln(x).
60MAXReturns the largest value in a set of values=MAX(number1number1 [required]: First numbers for which you want to find the maximum value., [number2number2 [optional]: Additional numbers.], ...)=MAX(number1, [number2], ...)
• number1 (required): First numbers for which you want to find the maximum value.
61MAXAReturns the largest value in a list of arguments=MAXA(value1value1 [optional]: Additional number arguments.value1 [required]: The first number argument for which you want to find the largest value., [value2], ...)=MAXA(value1, [value2], ...)
• value1 (required): The first number argument for which you want to find the largest value.
• value1 (optional): Additional number arguments.
62MAXIFSReturns the maximum value among cells specified by a given set of conditions or criteria=MAXIFS(max_rangemax_range [required]: The actual range of cells in which the maximum will be determined., criteria_range1criteria_range1 [required]: Is the set of cells to evaluate with the criteria., criteria1criteria1 [required]: Is the criteria in the form of a number, expression, or text that defines which cells will be evaluated as maximum., [criteria_range2, criteria2criteria_range2, criteria2 [optional]: Additional optional ranges/criterias], ...)=MAXIFS(max_range, criteria_range1, criteria1, [criteria_range2, criteria2], ...)
• max_range (required): The actual range of cells in which the maximum will be determined.
• criteria_range1 (required): Is the set of cells to evaluate with the criteria.
• criteria1 (required): Is the criteria in the form of a number, expression, or text that defines which cells will be evaluated as maximum.
• criteria_range2, criteria2 (optional): Additional optional ranges/criterias
63MEDIANReturns the median of the given numbers=MEDIAN(number1, [number2], ...)=MEDIAN(number1, [number2], ...)
64MINReturns the smallest number in a set of values=MIN(number1number1 [required]: First numbers for which you want to find the smallest value., [number2number2 [optional]: Additional numbers.], ...)=MIN(number1, [number2], ...)
• number1 (required): First numbers for which you want to find the smallest value.
65MINAReturns the smallest value in the list of arguments=MINA(value1value1 [required]: The first number argument for which you want to find the smallest value., [value2value2 [optional]: Additional number arguments.], ...)=MINA(value1, [value2], ...)
• value1 (required): The first number argument for which you want to find the smallest value.
• value2 (optional): Additional number arguments.
66MINIFSReturns the minimum value among cells specified by a given set of conditions or criteria=MINIFS(min_rangemin_range [required]: The actual range of cells in which the smallest number will be determined., criteria_range1criteria_range1 [required]: Is the set of cells to evaluate with the criteria., criteria1criteria1 [required]: Is the criteria in the form of a number, expression, or text that defines which cells will be evaluated as minimum., [criteria_range2, criteria2criteria_range2, criteria2 [optional]: Additional optional ranges/criterias], ...)=MINIFS(min_range, criteria_range1, criteria1, [criteria_range2, criteria2], ...)
• criteria_range2, criteria2 (optional): Additional optional ranges/criterias
• min_range (required): The actual range of cells in which the smallest number will be determined.
• criteria_range1 (required): Is the set of cells to evaluate with the criteria.
• criteria1 (required): Is the criteria in the form of a number, expression, or text that defines which cells will be evaluated as minimum.
67MODE.MULTReturns a vertical array of the most frequently occurring, or repetitive values in an array or range of data=MODE.MULT((number1number1 [required]: The first number argument for which you want to calculate the mode., [number2number2 [optional]: Additional numbers to calculate the mode.], ...)=MODE.MULT((number1, [number2], ...)
• number1 (required): The first number argument for which you want to calculate the mode.
• number2 (optional): Additional numbers to calculate the mode.
68MODE.SNGLReturns the most frequently occurring, or repetitive, value in an array or range of data=MODE.SNGL(number1number1 [required]: The first argument for which you want to calculate the mode., [number2number2 [optional]: Additional numbers to calculate the mode.], ...)=MODE.SNGL(number1, [number2], ...)
• number1 (required): The first argument for which you want to calculate the mode.
• number2 (optional): Additional numbers to calculate the mode.
69NEGBINOM.DISTReturns the negative binomial distribution, the probability that there will be Number_f failures before the Number_s-th success, with Probability_s probability of a success=NEGBINOM.DIST(number_fnumber_f [required]: The number of failures., number_snumber_s [required]: The threshold number of successes., probability_sprobability_s [required]: The probability of a success., cumulativecumulative [required]: A logical value that determines the form of the function.)=NEGBINOM.DIST(number_f, number_s, probability_s, cumulative)
• number_f (required): The number of failures.
• number_s (required): The threshold number of successes.
• probability_s (required): The probability of a success.
• cumulative (required): A logical value that determines the form of the function.
70NORM.DISTReturns the normal distribution for the specified mean and standard deviation=NORM.DIST(xx [required]: The value for which you want the distribution., meanmean [required]: The arithmetic mean of the distribution., standard_devstandard_dev [required]: The standard deviation of the distribution., cumulativecumulative [required]: A logical value that determines the form of the function.)=NORM.DIST(x, mean, standard_dev, cumulative)
• x (required): The value for which you want the distribution.
• mean (required): The arithmetic mean of the distribution.
• standard_dev (required): The standard deviation of the distribution.
• cumulative (required): A logical value that determines the form of the function.
71NORM.S.DISTReturns the standard normal distribution (has a mean of zero and a standard deviation of one)=NORM.S.DIST(zz [required]: The value for which you want the distribution., cumulativecumulative [required]: Cumulative is a logical value that determines the form of the function.)=NORM.S.DIST(z, cumulative)
• z (required): The value for which you want the distribution.
• cumulative (required): Cumulative is a logical value that determines the form of the function.
72NORM.S.INVReturns the inverse of the standard normal cumulative distribution=NORM.S.INV(probabilityprobability [required]: A probability corresponding to the normal distribution.)=NORM.S.INV(probability)
• probability (required): A probability corresponding to the normal distribution.
73NORMINVReturns the inverse of the normal cumulative distribution for the specified mean and standard deviation=NORMINV(probabilityprobability [required]: A probability corresponding to the normal distribution., meanmean [required]: The arithmetic mean of the distribution., standard_devstandard_dev [required]: The standard deviation of the distribution.)=NORMINV(probability, mean, standard_dev)
• probability (required): A probability corresponding to the normal distribution.
• mean (required): The arithmetic mean of the distribution.
• standard_dev (required): The standard deviation of the distribution.
74PEARSONReturns the Pearson product moment correlation coefficient, r, a dimensionless index that ranges from -1=PEARSON(array1array1 [required]: A set of independent values., array2array2 [required]: A set of dependent values.)=PEARSON(array1, array2)
• array1 (required): A set of independent values.
• array2 (required): A set of dependent values.
75PERCENTILE.EXCReturns the Pearson product moment correlation coefficient, r, a dimensionless index that ranges from -1=PERCENTILE.EXC(arrayarray [required]: The array or range of data that defines relative standing., kk [required]: The array or range of data that defines relative standing.)=PERCENTILE.EXC(array, k)
• array (required): The array or range of data that defines relative standing.
• k (required): The array or range of data that defines relative standing.
76PERCENTILE.INCReturns the k-th percentile of values in a range, where k is in the range 0=PERCENTILE.INC(arrayarray [required]: The array or range of data that defines relative standing., kk [required]: The percentile value in the range 0.)=PERCENTILE.INC(array, k)
• array (required): The array or range of data that defines relative standing.
• k (required): The percentile value in the range 0.
77PERCENTRANK.EXCReturns the rank of a value in a data set as a percentage (0=PERCENTRANK.EXC(arrayarray [required]: The array or range of data with numeric values that defines relative standing

, xx [required]: The value for which you want to know the rank., [significancesignificance [optional]: A value that identifies the number of significant digits for the returned percentage value.])
=PERCENTRANK.EXC(array, x, [significance])
• array (required): The array or range of data with numeric values that defines relative standing

• x (required): The value for which you want to know the rank.
• significance (optional): A value that identifies the number of significant digits for the returned percentage value.
78PERCENTRANK.INCReturns the rank of a value in a data set as a percentage (0=PERCENTRANK.INC(arrayarray [required]: The array or range of data with numeric values that defines relative standing., xx [required]: The value for which you want to know the rank., [significancesignificance [optional]: A value that identifies the number of significant digits for the returned percentage value.])=PERCENTRANK.INC(array, x, [significance])
• array (required): The array or range of data with numeric values that defines relative standing.
• x (required): The value for which you want to know the rank.
• significance (optional): A value that identifies the number of significant digits for the returned percentage value.
79PERMUTReturns the number of permutations for a given number of objects that can be selected from number objects=PERMUT(numbernumber [required]: An integer that describes the number of objects., number_chosennumber_chosen [required]: An integer that describes the number of objects in each permutation.)=PERMUT(number, number_chosen)
• number_chosen (required): An integer that describes the number of objects in each permutation.
• number (required): An integer that describes the number of objects.
80PERMUTATIONAReturns the number of permutations for a given number of objects (with repetitions) that can be selected from the total objects=PERMUTATIONA(numbernumber [required]: An integer that describes the total number of objects., number_chosennumber_chosen [required]: An integer that describes the number of objects in each permutation.)=PERMUTATIONA(number, number_chosen)
• number_chosen (required): An integer that describes the number of objects in each permutation.
• number (required): An integer that describes the total number of objects.
81PHIReturns the value of the density function for a standard normal distribution=PHI(xx [required]: X is the number for which you want the density of the standard normal distribution.)=PHI(x)
• x (required): X is the number for which you want the density of the standard normal distribution.
82POISSON.DISTReturns the Poisson distribution=POISSON.DIST(xx [required]: The number of events., meanmean [required]: The expected numeric value., cumulativecumulative [required]: A logical value that determines the form of the probability distribution returned.)=POISSON.DIST(x, mean, cumulative)
• x (required): The number of events.
• mean (required): The expected numeric value.
• cumulative (required): A logical value that determines the form of the probability distribution returned.
83PROBReturns the probability that values in a range are between two limits=PROB(x_rangex_range [required]: The range of numeric values of x with which there are associated probabilities., prob_rangeprob_range [required]: A set of probabilities associated with values in x_range., [lower_limitlower_limit [optional]: The lower bound on the value for which you want a probability.], [upper_limitupper_limit [optional]: The optional upper bound on the value for which you want a probability.])=PROB(x_range, prob_range, [lower_limit], [upper_limit])
• x_range (required): The range of numeric values of x with which there are associated probabilities.
• prob_range (required): A set of probabilities associated with values in x_range.
• lower_limit (optional): The lower bound on the value for which you want a probability.
• upper_limit (optional): The optional upper bound on the value for which you want a probability.
84QUARTILE.EXCReturns the quartile of the data set, based on percentile values from 0=QUARTILE.EXC(arrayarray [required]: The array or cell range of numeric values for which you want the quartile value., quartquart [required]: Indicates which value to return.)=QUARTILE.EXC(array, quart)
• array (required): The array or cell range of numeric values for which you want the quartile value.
• quart (required): Indicates which value to return.
85QUARTILE.INCReturns the quartile of a data set, based on percentile values from 0=QUARTILE.INC(arrayarray [required]: The array or cell range of numeric values for which you want the quartile value., quartquart [required]: Indicates which value to return.)=QUARTILE.INC(array, quart)
• array (required): The array or cell range of numeric values for which you want the quartile value.
• quart (required): Indicates which value to return.
86RANK.AVGReturns the rank of a number in a list of numbers: its size relative to other values in the list; if more than one value has the same rank, the average rank is returned=RANK.AVG(numbernumber [required]: The number whose rank you want to find., refref [required]: An array of, or a reference to, a list of numbers., [orderorder [optional]: A number specifying how to rank number.])=RANK.AVG(number, ref, [order])
• number (required): The number whose rank you want to find.
• ref (required): An array of, or a reference to, a list of numbers.
• order (optional): A number specifying how to rank number.
87RANK.EQReturns the rank of a number in a list of numbers=RANK.EQ(numbernumber [required]: The number whose rank you want to find., refref [required]: An array of, or a reference to, a list of numbers., [orderorder [optional]: A number specifying how to rank number.])=RANK.EQ(number, ref, [order])
• number (required): The number whose rank you want to find.
• ref (required): An array of, or a reference to, a list of numbers.
• order (optional): A number specifying how to rank number.
88RSQReturns the square of the Pearson product moment correlation coefficient through data points in known_y's and known_x's=RSQ(known_y'sknown_y's [required]: An array or range of data points., known_x'sknown_x's [required]: An array or range of data points.)=RSQ(known_y's, known_x's)
• known_y's (required): An array or range of data points.
• known_x's (required): An array or range of data points.
89SKEWReturns the skewness of a distribution=SKEW(number1number1 [required]: Number for which you want to calculate skewness., [number2number2 [optional]: Additional optional numbers.], ...)=SKEW(number1, [number2], ...)
• number1 (required): Number for which you want to calculate skewness.
• number2 (optional): Additional optional numbers.
90SKEW.PReturns the skewness of a distribution based on a population=SKEW.P(number1number1 [required]: Number for which you want to calculate population skewness., [number2number2 [optional]: Additional optional numbers.], ...)=SKEW.P(number1, [number2], ...)
• number1 (required): Number for which you want to calculate population skewness.
• number2 (optional): Additional optional numbers.
91SLOPEReturns the slope of the linear regression line through data points in known_y's and known_x's=SLOPE(known_y'sknown_y's [required]: An array or cell range of numeric dependent data points., known_x'sknown_x's [required]: The set of independent data points.)=SLOPE(known_y's, known_x's)
• known_y's (required): An array or cell range of numeric dependent data points.
• known_x's (required): The set of independent data points.
92SMALLReturns the k-th smallest value in a data set=SMALL(arrayarray [required]: An array or range of numerical data for which you want to determine the k-th smallest value., kk [required]: The position (from the smallest) in the array or range of data to return.)=SMALL(array, k)
• array (required): An array or range of numerical data for which you want to determine the k-th smallest value.
• k (required): The position (from the smallest) in the array or range of data to return.
93STANDARDIZEReturns a normalized value from a distribution characterized by mean and standard_dev=STANDARDIZE(xx [required]: The value you want to normalize., meanmean [required]: The arithmetic mean of the distribution., standard_devstandard_dev [required]: The standard deviation of the distribution.)=STANDARDIZE(x, mean, standard_dev)
• x (required): The value you want to normalize.
• mean (required): The arithmetic mean of the distribution.
• standard_dev (required): The standard deviation of the distribution.
94STDEV.PCalculates standard deviation based on the entire population given as arguments (ignores logical values and text)=STDEV.P(number1number1 [required]: The first number argument corresponding to a population., [number2number2 [optional]: Additional numbers arguments], ...)=STDEV.P(number1, [number2], ...)
• number1 (required): The first number argument corresponding to a population.
• number2 (optional): Additional numbers arguments
95STDEV.SEstimates standard deviation based on a sample (ignores logical values and text in the sample)=STDEV.S(number1number1 [required]: The first number argument corresponding to a sample of a population., [number2number2 [optional]: Additional optional numbers], ...)=STDEV.S(number1, [number2], ...)
• number1 (required): The first number argument corresponding to a sample of a population.
• number2 (optional): Additional optional numbers
96STDEVAEstimates standard deviation based on a sample=STDEVA(value1, [value2], ...)=STDEVA(value1, [value2], ...)
97STDEVPACalculates standard deviation based on the entire population given as arguments, including text and logical values=STDEVPA(value1value1 [required]: The first value corresponding to a sample of a population., [value2value2 [optional]: Additional optional values], ...)=STDEVPA(value1, [value2], ...)
• value1 (required): The first value corresponding to a sample of a population.
• value2 (optional): Additional optional values
98STEYXReturns the standard error of the predicted y-value for each x in the regression=STEYX(known_y'sknown_y's [required]: An array or range of dependent data points., known_x'sknown_x's [required]: An array or range of independent data points.)=STEYX(known_y's, known_x's)
• known_y's (required): An array or range of dependent data points.
• known_x's (required): An array or range of independent data points.
99T.DISTReturns the Student's left-tailed t-distribution=T.DIST(xx [required]: The numeric value at which to evaluate the distribution

, deg_freedomdeg_freedom [required]: An integer indicating the number of degrees of freedom., cumulativecumulative [required]: A logical value that determines the form of the function.)
=T.DIST(x, deg_freedom, cumulative)
• x (required): The numeric value at which to evaluate the distribution

• deg_freedom (required): An integer indicating the number of degrees of freedom.
• cumulative (required): A logical value that determines the form of the function.
100T.DIST.2TReturns the two-tailed Student's t-distribution=T.DIST.2T(xx [required]: The numeric value at which to evaluate the distribution., deg_freedomdeg_freedom [required]: An integer indicating the number of degrees of freedom.)=T.DIST.2T(x, deg_freedom)
• x (required): The numeric value at which to evaluate the distribution.
• deg_freedom (required): An integer indicating the number of degrees of freedom.
101T.DIST.RTReturns the right-tailed Student's t-distribution=T.DIST.RT(xx [required]: The numeric value at which to evaluate the distribution., deg_freedomdeg_freedom [required]: An integer indicating the number of degrees of freedom.)=T.DIST.RT(x, deg_freedom)
• x (required): The numeric value at which to evaluate the distribution.
• deg_freedom (required): An integer indicating the number of degrees of freedom.
102T.INVReturns the two-tailed inverse of the Student's t-distribution=T.INV(probabilityprobability [required]: The probability associated with the Student's t-distribution., deg_freedomdeg_freedom [required]: The number of degrees of freedom with which to characterize the distribution.)=T.INV(probability, deg_freedom)
• probability (required): The probability associated with the Student's t-distribution.
• deg_freedom (required): The number of degrees of freedom with which to characterize the distribution.
103T.INV.2TReturns the two-tailed inverse of the Student's t-distribution=T.INV.2T(probabilityprobability [required]: The probability associated with the Student's t-distribution., deg_freedomdeg_freedom [required]: The number of degrees of freedom with which to characterize the distribution.)=T.INV.2T(probability, deg_freedom)
• probability (required): The probability associated with the Student's t-distribution.
• deg_freedom (required): The number of degrees of freedom with which to characterize the distribution.
104T.TESTReturns the probability associated with a Student's t-Test=T.TEST(array1array1 [required]: The first data set., array2array2 [required]: The second data set., tailstails [required]: Specifies the number of distribution tails., typetype [required]: The kind of t-Test to perform.)=T.TEST(array1, array2, tails, type)
• array1 (required): The first data set.
• array2 (required): The second data set.
• tails (required): Specifies the number of distribution tails.
• type (required): The kind of t-Test to perform.
105TRENDReturns values along a linear trend=TREND(known_y'sknown_y's [required]: The set of y-values you already know in the relationship y = mx + b, [known_x'sknown_x's [optional]: An optional set of x-values that you may already know in the relationship y = mx + b], [new_x'snew_x's [optional]: New x-values for which you want TREND to return corresponding y-values], [constconst [optional]: A logical value specifying whether to force the constant b to equal 0])=TREND(known_y's, [known_x's], [new_x's], [const])
• known_y's (required): The set of y-values you already know in the relationship y = mx + b
• known_x's (optional): An optional set of x-values that you may already know in the relationship y = mx + b
• new_x's (optional): New x-values for which you want TREND to return corresponding y-values
• const (optional): A logical value specifying whether to force the constant b to equal 0
106TRIMMEANReturns the mean of the interior of a data set=TRIMMEAN(arrayarray [required]: The array or range of values to trim and average., percentpercent [required]: The fractional number of data points to exclude from the calculation.)=TRIMMEAN(array, percent)
• array (required): The array or range of values to trim and average.
• percent (required): The fractional number of data points to exclude from the calculation.
107VAR.PCalculates variance based on the entire population (ignores logical values and text in the population)=VAR.P(number1number1 [required]: The first number argument corresponding to a population., [number2number2 [optional]: Additional optional numbers.], ...)=VAR.P(number1, [number2], ...)
• number1 (required): The first number argument corresponding to a population.
• number2 (optional): Additional optional numbers.
108VAR.SEstimates variance based on a sample (ignores logical values and text in the sample)=VAR.S(number1number1 [required]: The first number argument corresponding to a sample of a population., [number2number2 [optional]: Additional optional numbers.], ...)=VAR.S(number1, [number2], ...)
• number1 (required): The first number argument corresponding to a sample of a population.
• number2 (optional): Additional optional numbers.
109VARAEstimates variance based on a sample=VARA(value1value1 [required]: The first value arguments corresponding to a sample of a population., [value2value2 [optional]: Additional optional value arguments.], ...)=VARA(value1, [value2], ...)
• value1 (required): The first value arguments corresponding to a sample of a population.
• value2 (optional): Additional optional value arguments.
110VARPACalculates variance based on the entire population=VARPA(value1value1 [required]: The first value arguments corresponding to a sample of a population., [value2value2 [optional]: Additional optional value arguments.], ...)=VARPA(value1, [value2], ...)
• value1 (required): The first value arguments corresponding to a sample of a population.
• value2 (optional): Additional optional value arguments.
111WEIBULL.DISTReturns the Weibull distribution=WEIBULL.DIST(xx [required]: The value at which to evaluate the function., alphaalpha [required]: A parameter to the distribution., betabeta [required]: A parameter to the distribution., cumulativecumulative [required]: Determines the form of the function.)=WEIBULL.DIST(x, alpha, beta, cumulative)
• x (required): The value at which to evaluate the function.
• alpha (required): A parameter to the distribution.
• beta (required): A parameter to the distribution.
• cumulative (required): Determines the form of the function.
112Z.TESTReturns the one-tailed P-value of a z-test=Z.TEST(arrayarray [required]: The array or range of data against which to test x., xx [required]: The value to test., [sigmasigma [optional]: The population (known) standard deviation.])=Z.TEST(array, x, [sigma])
• array (required): The array or range of data against which to test x.
• x (required): The value to test.
• sigma (optional): The population (known) standard deviation.

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