Statistical Functions

Count & Frequency. Finding the Largest & Smallest Values. Permutations. Confidence Intervals. Averages. Percentiles, Quartiles & Rank. Distribution & Tests of Probability. Deviation & Variance. Trend Line 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)
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    value1 (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)
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    value1 (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)
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    value1 (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")
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    criteria_range (range)
    The range to check against criterion.
    criterion (string)
    The pattern or test to apply to criteria_range.
  • BETADIST

    The BETADIST function returns the probability of a given value as defined by the beta distribution function.

    BETADIST(0.42, 3, 8)
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    value (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)
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    probability (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.
  • 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)
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    value (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)
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    probability (number)
    The probability at which to evaluate the function.
    alpha (number)
    The first shape parameter of the distribution.
  • 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)
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    num_successes (number)
    The number of successes for which to calculate the probability in num_trials trials.
    num_trials (number)
    The number of independent trials.
  • 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)
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    num_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)
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    x (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)
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    probability (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)
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    alpha (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})
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    data_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)
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    value1 (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)
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    value1 (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})
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    value1 (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")
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    range (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})
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    data_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)
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    num_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)
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    value1 (number)
    The first value or range of the sample.
    value2 (number)
    Additional values or ranges to include in the sample.
  • EXPONDIST

    Returns the value of the exponential distribution function with a specified lambda at a specified value.

    EXPONDIST(4,0.5,FALSE)
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    x (number)
    The input to the exponential distribution function.
    lambda (number)
    The lambda to specify the exponential distribution function.
  • EXPON_DIST

    Returns the value of the exponential distribution function with a specified lambda at a specified value.

    EXPON_DIST(4,0.5,FALSE)
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    x (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)
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    value (number)
    The value for which to calculate the Fisher transformation.
  • FISHERINV

    Returns the inverse Fisher transformation of a specified value.

    FISHERINV(0.962)
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    value (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})
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    x (number)
    The value on the x-axis to forecast.
    data_y (array)
    The range representing the array or matrix of dependent data.
  • GAMMADIST

    The GAMMADIST function calculates the gamma distribution, a 2-parameter continuous probability distribution.

    GAMMADIST(4.79, 1.234, 7, TRUE)
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    x (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)
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    probability (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).

    value (number)
    The input to the Gamma function. The natural logarithm of Gamma (value) will be returned. Value must be positive.
  • GAMMA_DIST

    The GAMMA_DIST function calculates the gamma distribution, a 2-parameter continuous probability distribution.

    GAMMA_DIST(4.79, 1.234, 7, TRUE)
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    x (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)
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    probability (number)
    The input to the inverse gamma distribution function.
    alpha (number)
    The shape of the gamma distribution.
  • GEOMEAN

    Calculates the geometric mean of a dataset.

    GEOMEAN(1,2,3,4,5,6,7,8,9,10)
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    value1 (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})
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    known_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)
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    value1 (number)
    The first value or range of the population.
    value2 (number)
    Additional values or ranges to include in the population.
  • 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)
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    num_successes (number)
    The desired number of successes.
    num_draws (number)
    The number of permitted draws.
  • 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)
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    num_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})
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    data_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)
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    value1 (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)
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    data (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})
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    known_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})
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    known_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)
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    x (number)
    The input to the inverse log-normal cumulative distribution function.
    mean (number)
    The mean (mu) of the inverse 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)
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    x (number)
    The input to the log-normal cumulative distribution function.
    mean (number)
    The mean (mu) of the 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)
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    x (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)
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    value1 (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)
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    value1 (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")
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    range (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)
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    value1 (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)
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    value1 (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)
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    value1 (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")
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    range (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)
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    value1 (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)
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    number1 (number)
    A number.
    number2 (number)
    Optional: A number
  • 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)
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    num_failures (number)
    The number of failures to model.
    num_successes (number)
    The number of successes to model.
  • 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)
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    num_failures (number)
    The number of failures to model.
    num_successes (number)
    The number of successes to model.
  • 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)
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    x (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)
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    x (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)
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    x (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)
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    x (number)
    The input to the inverse standard normal distribution function.
  • 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)
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    x (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)
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    x (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)
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    x (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})
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    data_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)
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    data (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)
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    data (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)
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    n (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)
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    x (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)
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    data (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})
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    value (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})
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    data_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)
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    value1 (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})
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    data_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)
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    data (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)
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    value (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)
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    value1 (number)
    The first value or range of the sample.
    value2 (number)
    Optional: Additional values or ranges to include in the sample.
  • 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)
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    value1 (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)
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    value1 (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)
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    value1 (number)
    The first value or range of the population.
    value2 (number)
    Additional values or ranges to include in the population.
  • 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})
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    number1 (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})
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    number1 (mixed)
    First number or reference in the sample.
    number2 (mixed)
    Optional: Second number or reference.
  • STEYX

    Calculates the standard error of the predicted y-value for each x in the regression of a dataset.

    STEYX({var1},{var2})
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    data_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.
  • TDIST

    Calculates the probability for Student's t-distribution with a given input (x).

    TDIST(0.5, 1, 2)
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    x (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)
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    probability (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})
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    known_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.
  • 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)
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    data (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.
  • T_DIST

    Returns the Percentage Points (probability) for the Student t-distribution

    T_DIST(1.96, 60, FALSE)
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    x (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)
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    probability (number)
    The probability associated with the t-distribution.
    degrees_freedom (number)
    The number of degrees of freedom.
  • VAR

    Calculates the variance based on a sample.

    VAR(1,2,3,4,5,6,7,8,9,10)
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    value1 (number)
    The first value or range of the sample.
    value2 (number)
    Additional values or ranges to include in the sample.
  • 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)
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    value1 (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)
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    value1 (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)
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    value1 (number)
    The first value or range of the population.
    value2 (number)
    Additional values or ranges to include in the population.
  • 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})
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    number1 (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})
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    number1 (mixed)
    First number or reference.
    number2 (mixed)
    Optional: Second number or reference.
  • 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)
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    x (number)
    The input to the Weibull distribution function.
    shape (number)
    The shape parameter of the Weibull distribution function.
  • 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)
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    data (array)
    The array or range containing the dataset to consider.
    value (number)
    The test statistic to use in the Z-test.
  • Z_TEST

    Returns the one-tailed probability-value of a z-test

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