Statistical Functions

This function returns values from the lognormal distribution where the natural logarithm of the random variable follows a normal distribution with parameters μ (mean) and σ (standard_dev). The probability density function (PDF) is given by: Syntax LOGNORM.DIST(x; mean; standard_dev; cumulative)

This function returns the quantile (inverse cumulative distribution) of a lognormal distribution, where the natural logarithm of the random variable *x* is normally distributed with specified mean and standard deviation parameters. If: p = LOGNORM.DIST(x, mean, standard_dev, TRUE) Then: x = LOGNORM.INV(p,

This function calculates the exponential curve in regression analyses and returns an array of values describing this curve. Since it returns an array, it must be entered as an array formula. Syntax: LOGEST(known_y’s, known_x’s, const, stats) Arguments: known_y’s(required): The known

This function returns the k-th largest value in a data set. Use this function to select a value based on its relative size. For example, you can use LARGE() to calculate the top three sales in a table. Syntax: LARGE(array, k) Arguments array (required): The array

Returns the kurtosis of a dataset, which measures the « tailedness » and peakedness of a distribution compared to a normal distribution. Syntax: KURT(number1; [number2]; …) Arguments Argument Required? Description number1 Yes First data point or range. number2, … Optional Additional data points Notes:

Calculates the y-intercept of the linear regression line fitted to a dataset. This is the point where the regression line crosses the y-axis (i.e., the predicted value of y when x = 0). Syntax: INTERCEPT(known_y’s; known_x’s) Arguments Argument Required? Description known_y’s Yes Dependent variable (response

This function returns probabilities for a hypergeometrically distributed random variable. It calculates the probability of obtaining a specific number of successes in a sample drawn from a finite population without replacement. Syntax: HYPGEOM.DIST(sample_s; number_sample; population_s; number_population; cumulative) Required Information: Number

Returns the harmonic mean of a dataset, which is the reciprocal of the arithmetic average of reciprocals. Syntax: HARMEAN(number1; [number2]; …) Arguments number1 (required) – First value or range for calculation. number2, … (optional) – Additional values or ranges. Can use a single array

The GROWTH function calculates predicted values based on an exponential trend. It returns y-values corresponding to a specified set of new x-values using existing x and y data. It can also fit an exponential curve to known data points. Syntax: GROWTH(known_y’s; known_x’s;

This function returns the geometric mean of a set of positive numbers. The geometric mean is particularly useful for calculating average growth rates (e.g., compound interest, variable returns, or percentage-based trends). The geometric mean is computed as the n-th root of the product of all values, where n is the number

This function returns the natural logarithm of the gamma function, Γ(x), calculated with 15-digit precision. Syntax: =GAMMALN.PRECISE(x) Arguments: x (required) – The value for which you want to calculate GAMMALN.PRECISE(). Background: For background details, refer to the documentation for the GAMMALN() function. Example: For usage examples

This function returns the natural logarithm of the gamma function, Γ(*x*). Syntax: GAMMALN(x) Arguments: x (required) – The value for which you want to calculate GAMMALN(). Background: The GAMMALN() function returns the natural logarithm (base *e*) of the gamma function. A logarithm of a number is the

This function returns the probabilities of a gamma-distributed random variable. It is used to analyze variables with a skewed distribution, commonly applied in queuing theory and other statistical analyses. Syntax GAMMA.DIST(x; alpha; beta; cumulative) Arguments x (required) – The value (quantile)

This function returns the quantile of the gamma distribution. If p = GAMMA.DIST(x,…), then GAMMA.INV(p,…) = x. Use this function to examine a variable whose distribution may be skewed. Syntax: GAMMA.INV(probability; alpha; beta) Arguments: probability(required) – A probability associated with the

This function returns a frequency distribution as a single-column matrix. For example, use FREQUENCY() to count sales within a specific range. Since FREQUENCY() returns an array of values, it must be entered as an array formula. Syntax FREQUENCY(data_array; bins_array) Arguments data_array (required) – An array