sheetxl/assets/CTFVn3t9DGwXwk7i.js

SheetXL - Command line tool

/**
 * @license sheetxl - SheetXL - Command line tool - v0.6.0
 *
 * (C) 2025-present SheetXL Inc. & Michael T. Ford
 * License: The license can be found at https://www.sheetxl.com/license.
 */
const e = { "BETA.DIST": { s: "Returns the beta probability distribution function", p: { x: { d: "is the value between A and B at which to evaluate the function" }, alpha: { d: "is a parameter to the distribution and must be greater than 0" }, beta: { d: "is a parameter to the distribution and must be greater than 0" }, cumulative: { d: "is a logical value: for the cumulative distribution function, use TRUE; for the probability density function, use FALSE" }, a: { d: "lower bound to the interval of x", v: 0 }, b: { d: "upper bound to the interval of x", v: 1 } } }, BETADIST: { s: "Returns the cumulative beta probability density function", p: { x: { d: "is the value between A and B at which to evaluate the function" }, alpha: { d: "is a parameter to the distribution and must be greater than 0" }, beta: { d: "is a parameter to the distribution and must be greater than 0" }, cumulative: { d: "is a logical value: for the cumulative distribution function, use TRUE; for the probability density function, use FALSE" }, a: { d: "lower bound to the interval of x", v: 0 }, b: { d: "upper bound to the interval of x", v: 1 } }, c: "Compatibility" }, "BETA.INV": { s: "Returns the inverse of the cumulative beta probability density function (BETA.DIST)", p: { probability: { d: "is a probability associated with the beta distribution" }, alpha: { d: "is a parameter to the distribution and must be greater than 0" }, beta: { d: "is a parameter to the distribution and must be greater than 0" }, a: { d: "lower bound to the interval of x", v: 0 }, b: { d: "upper bound to the interval of x", v: 1 } } }, BETAINV: { s: "Returns the inverse of the cumulative beta probability density function (BETADIST)", p: { probability: { d: "is a probability associated with the beta distribution" }, alpha: { d: "is a parameter to the distribution and must be greater than 0" }, beta: { d: "is a parameter to the distribution and must be greater than 0" }, a: { d: "lower bound to the interval of x", v: 0 }, b: { d: "upper bound to the interval of x", v: 1 } }, c: "Compatibility" }, "BINOM.DIST": { s: "Returns the individual term binomial distribution probability", p: { numberS: { d: "is the number of successes in trials" }, trials: { d: "is the number of independent trials" }, probabilityS: { d: "is the probability of success on each trial" }, cumulative: { d: "is a logical value: for the cumulative distribution function, use TRUE; for the probability mass function, use FALSE" } } }, BINOMDIST: { s: "Returns the individual term binomial distribution probability", p: { numberS: { d: "is the number of successes in trials" }, trials: { d: "is the number of independent trials" }, probabilityS: { d: "is the probability of success on each trial" }, cumulative: { d: "is a logical value: for the cumulative distribution function, use TRUE; for the probability mass function, use FALSE" } }, c: "Compatibility" }, "BINOM.DIST.RANGE": { s: "Returns the probability of a trial result using a binomial distribution", p: { trials: { d: "is the number of independent trials" }, probabilityS: { d: "is the probability of success on each trial" }, numberS: { d: "is the number of successes in trials" }, numberS2: { d: "if provided this function returns the probability that the number of successful trials shall lie between number_s and number_s2", v: "numberS" } } }, "BINOM.INV": { s: "Returns the smallest value for which the cumulative binomial distribution is greater than or equal to a criterion value", p: { trials: { d: "is the number of Bernoulli trials" }, probabilityS: { d: "is the probability of success on each trial, a number between 0 and 1 inclusive" }, alpha: { d: "is the criterion value, a number between 0 and 1 inclusive" } } }, "CHISQ.DIST": { s: "Returns the left-tailed probability of the chi-squared distribution", p: { x: { d: "is the value at which you want to evaluate the distribution, a nonnegative number" }, degFreedom: { d: "is the number of degrees of freedom, a number between 1 and 10^10, excluding 10^10" }, cumulative: { d: "is a logical value for the function to return: the cumulative distribution function = TRUE; the probability density function = FALSE", v: false } } }, CHIDIST: { s: "Returns the right-tailed probability of the chi-squared distribution", p: { x: { d: "is the value at which you want to evaluate the distribution, a nonnegative number" }, degFreedom: { d: "is the number of degrees of freedom, a number between 1 and 10^10, excluding 10^10" } }, c: "Compatibility" }, "CHISQ.DIST.RT": { s: "Returns the right-tailed probability of the chi-squared distribution", p: { x: { d: "is the value at which you want to evaluate the distribution, a nonnegative number" }, degFreedom: { d: "is the number of degrees of freedom, a number between 1 and 10^10, excluding 10^10" } } }, "CHISQ.INV": { s: "Returns the inverse of the left-tailed probability of the chi-squared distribution", p: { probability: { d: "is a probability associated with the chi-squared distribution, a value between 0 and 1 inclusive" }, degFreedom: { d: "is the number of degrees of freedom, a number between 1 and 10^10, excluding 10^10" } } }, CHIINV: { s: "Returns the inverse of the right-tailed probability of the chi-squared distribution", p: { probability: { d: "is a probability associated with the chi-squared distribution, a value between 0 and 1 inclusive" }, degFreedom: { d: "is the number of degrees of freedom, a number between 1 and 10^10, excluding 10^10" } }, c: "Compatibility" }, "CHISQ.INV.RT": { s: "Returns the right-tailed probability of the chi-squared distribution", p: { probability: { d: "is a probability associated with the chi-squared distribution, a value between 0 and 1 inclusive" }, degFreedom: { d: "is the number of degrees of freedom, a number between 1 and 10^10, excluding 10^10" } } }, "CHISQ.TEST": { s: "Returns the test for independence: the value from the chi-squared distribution for the statistic and the appropriate degrees of freedom", p: { actualRange: { d: "is the range of data that contains observations to test against expected values" }, expectedRange: { d: "is the range of data that contains the ratio of the product of row totals and column totals to the grand total" } } }, CHITEST: { s: "Returns the test for independence: the value from the chi-squared distribution for the statistic and the appropriate degrees of freedom", p: { actualRange: { d: "is the range of data that contains observations to test against expected values" }, expectedRange: { d: "is the range of data that contains the ratio of the product of row totals and column totals to the grand total" } }, c: "Compatibility" }, "CONFIDENCE.NORM": { s: "Returns the confidence interval for a population mean, using a normal distribution", p: { alpha: { d: "is the significance level used to compute the confidence level, a number greater than 0 and less than 1" }, standardDev: { d: "is the population standard deviation for the data range and is assumed to be known. Standard_dev must be greater than 0" }, size: { d: "is the sample size" } } }, CONFIDENCE: { s: "Returns the confidence interval for a population mean, using a normal distribution", p: { alpha: { d: "is the significance level used to compute the confidence level, a number greater than 0 and less than 1" }, std: { d: "is the population standard deviation for the data range and is assumed to be known. Standard_dev must be greater than 0" }, dev: { d: "is the population standard deviation for the data range and is assumed to be known. Standard_dev must be greater than 0" }, size: { d: "is the sample size" } }, c: "Compatibility" }, "CONFIDENCE.T": { s: "Returns the confidence interval for a population mean, using a Student's T distribution", p: { alpha: { d: "is the significance level used to compute the confidence level, a number greater than 0 and less than 1" }, standardDev: { d: "is the population standard deviation for the data range and is assumed to be known. Standard_dev must be greater than 0" }, size: { d: "is the sample size" } } }, CORREL: { s: "Returns the correlation coefficient between two data sets", p: { array1: { d: "is a cell range of values. The values should be numbers, names, arrays, or references that contain numbers" }, array2: { d: "is a second cell range of values. The values should be numbers, names, arrays, or references that contain numbers" } } }, "COVARIANCE.P": { s: "Returns population covariance, the average of the products of deviations for each data point pair in two data sets", p: { array1: { d: "is the first cell range of integers and must be numbers, arrays, or references that contain numbers" }, array2: { d: "is the second cell range of integers and must be numbers, arrays, or references that contain numbers" } } }, COVARIANCE: { s: "Returns population covariance, the average of the products of deviations for each data point pair in two data sets", p: { array1: { d: "is the first cell range of integers and must be numbers, arrays, or references that contain numbers" }, array2: { d: "is the second cell range of integers and must be numbers, arrays, or references that contain numbers" } }, c: "Compatibility" }, "COVARIANCE.S": { s: "Returns sample covariance, the average of the products of deviations for each data point pair in two data sets", p: { array1: { d: "is the first cell range of integers and must be numbers, arrays, or references that contain numbers" }, array2: { d: "is the second cell range of integers and must be numbers, arrays, or references that contain numbers" } } }, CRITBINOM: { s: "Returns the smallest value for which the cumulative binomial distribution is greater than or equal to a criterion value", p: { trails: { d: "is the number of Bernoulli trials" }, probabilityS: { d: "is the probability of success on each trial, a number between 0 and 1 inclusive" }, alpha: { d: "is the criterion value, a number between 0 and 1 inclusive" } }, c: "Compatibility", h: "Unimplemented" }, DEVSQ: { s: "Returns the sum of squares of deviations of data points from their sample mean", p: { number: { d: "number1,number2,... are 1 to 255 arguments, or an array or array reference, on which you want DEVSQ to calculate" } } }, "EXPON.DIST": { s: "Returns the exponential distribution", p: { x: { d: "is the value of the function, a nonnegative number" }, lambda: { d: "is the parameter value, a positive number" }, cumulative: { d: "is a logical value for the function to return: the cumulative distribution function = TRUE; the probability density function = FALSE" } } }, EXPONDIST: { s: "Returns the exponential distribution", p: { x: { d: "is the value of the function, a nonnegative number" }, lambda: { d: "is the parameter value, a positive number" }, cumulative: { d: "is a logical value for the function to return: the cumulative distribution function = TRUE; the probability density function = FALSE" } }, c: "Compatibility" }, "F.DIST": { s: "Returns the (left-tailed) F probability distribution (degree of diversity) for two data sets", p: { x: { d: "is the value at which to evaluate the function, a nonnegative number" }, degFreedom1: { d: "is the numerator degrees of freedom, a number between 1 and 10^10, excluding 10^10" }, degFreedom2: { d: "is the denominator degrees of freedom, a number between 1 and 10^10, excluding 10^10" }, cumulative: { d: "is a logical value for the function to return: the cumulative distribution function = TRUE; the probability density function = FALSE" } } }, FDIST: { s: "Returns the (left-tailed) F probability distribution (degree of diversity) for two data sets", p: { x: { d: "is the value at which to evaluate the function, a nonnegative number" }, degFreedom1: { d: "is the numerator degrees of freedom, a number between 1 and 10^10, excluding 10^10" }, degFreedom2: { d: "is the denominator degrees of freedom, a number between 1 and 10^10, excluding 10^10" } }, c: "Compatibility" }, "F.DIST.RT": { s: "Returns the (right-tailed) F probability distribution (degree of diversity) for two data sets", p: { x: { d: "is the value at which to evaluate the function, a nonnegative number" }, degFreedom1: { d: "is the numerator degrees of freedom, a number between 1 and 10^10, excluding 10^10" }, degFreedom2: { d: "is the denominator degrees of freedom, a number between 1 and 10^10, excluding 10^10" } } }, "F.INV": { s: "Returns the inverse of the (left-tailed) F probability distribution: if p = F.DIST(x,...), then F.INV(p,...) = x", p: { probability: { d: "is a probability associated with the F cumulative distribution, a number between 0 and 1 inclusive" }, degFreedom1: { d: "is the numerator degrees of freedom, a number between 1 and 10^10, excluding 10^10" }, degFreedom2: { d: "is the denominator degrees of freedom, a number between 1 and 10^10, excluding 10^10" } } }, FINV: { s: "Returns the inverse of the (left-tailed) F probability distribution: if p = F.DIST(x,...), then F.INV(p,...) = x", p: { probability: { d: "is a probability associated with the F cumulative distribution, a number between 0 and 1 inclusive" }, degFreedom1: { d: "is the numerator degrees of freedom, a number between 1 and 10^10, excluding 10^10" }, degFreedom2: { d: "is the denominator degrees of freedom, a number between 1 and 10^10, excluding 10^10" } }, c: "Compatibility" }, "F.INV.RT": { s: "Returns the inverse of the (right-tailed) F probability distribution: if p = F.DIST.RT(x,...), then F.INV.RT(p,...) = x", p: { probability: { d: "is a probability associated with the F cumulative distribution, a number between 0 and 1 inclusive" }, degFreedom1: { d: "is the numerator degrees of freedom, a number between 1 and 10^10, excluding 10^10" }, degFreedom2: { d: "is the denominator degrees of freedom, a number between 1 and 10^10, excluding 10^10" } } }, "F.TEST": { s: "Returns the result of an F-test, the two-tailed probability that the variances in Array1 and Array2 are not significantly different", p: { array1: { d: "is the first array or range of data and can be numbers or names, arrays, or references that contain numbers (blanks are ignored)" }, array2: { d: "is the second array or range of data and can be numbers or names, arrays, or references that contain numbers (blanks are ignored)" } } }, FTEST: { s: "Returns the result of an F-test, the two-tailed probability that the variances in Array1 and Array2 are not significantly different", p: { array1: { d: "is the first array or range of data and can be numbers or names, arrays, or references that contain numbers (blanks are ignored)" }, array2: { d: "is the second array or range of data and can be numbers or names, arrays, or references that contain numbers (blanks are ignored)" } }, c: "Compatibility" }, FISHER: { s: "Returns the Fisher transformation", p: { x: { d: "is the value for which you want the transformation, a number between -1 and 1, excluding -1 and 1" } } }, FISHERINV: { s: "Returns the inverse of the Fisher transformation: if y = FISHER(x), then FISHERINV(y) = x", p: { y: { d: "is the value for which you want to perform the inverse of the transformation" } } }, FORECAST: { s: "Calculates, or predicts, a future value along a linear trend by using existing values", p: { x: { d: "is the data point for which you want to predict a value and must be a numeric value" }, knownYs: { d: "is the dependent array or range of numeric data", v: null }, knownXs: { d: "is the independent array or range of numeric data. The variance of Known_x's must not be zero", v: null } }, c: "Compatibility" }, "FORECAST.ETS": { s: "Returns the forecasted value for a specific future target date using exponential smoothing method", p: { targetDate: { d: "is the data point for which you want to predict a value. It should carry on the pattern of values in the timeline" }, values: { d: "is the array or range of numberic data you're predicting" }, timeline: { d: "is the independent array or range of numberic data. The dtes in the timeline must have a consistent step between them and can't be zero." }, seasonality: { d: "is a number value that indicates the length of the seasonal pattern in the data. It must be between 0 and 1, where 0 means no seasonality and 1 means full seasonality", v: 0.95 }, dataCompletion: { d: "is a number value that indicates how much of the data is complete. It must be between 0 and 1, where 0 means no data is complete and 1 means all data is complete", v: 1 }, aggregation: { d: "is a number value that indicates how the data should be aggregated. It must be between 0 and 1, where 0 means no aggregation and 1 means full aggregation", v: 1 } }, h: "Unimplemented" }, "FORECAST.ETS.CONFINT": { s: "Retursn a confidence interval for the forecasted value at the specified target date", p: { targetDate: { d: "is the data point for which you want to predict a value. It should carry on the pattern of values in the timeline" }, values: { d: "is the array or range of numberic data you're predicting" }, timeline: { d: "is the independent array or range of numberic data. The dtes in the timeline must have a consistent step between them and can't be zero." }, confiedenceLevel: { d: "is a number value that indicates the confidence level for the forecasted value. It must be between 0 and 1, where 0 means no confidence and 1 means full confidence", v: 0.95 }, seasonality: { d: "is a number value that indicates the length of the seasonal pattern in the data. It must be between 0 and 1, where 0 means no seasonality and 1 means full seasonality", v: 0.95 }, dataCompletion: { d: "is a number value that indicates how much of the data is complete. It must be between 0 and 1, where 0 means no data is complete and 1 means all data is complete", v: 1 }, aggregation: { d: "is a number value that indicates how the data should be aggregated. It must be between 0 and 1, where 0 means no aggregation and 1 means full aggregation", v: 1 } }, h: "Unimplemented" }, "FORECAST.ETS.SEASONALITY": { s: "Returns the length of the repetitive pattern in the data for the specific time series.", p: { values: { d: "is the array or range of numberic data you're predicting" }, timeline: { d: "is the independent array or range of numberic data. The dtes in the timeline must have a consistent step between them and can't be zero." }, dataCompletion: { d: "is a number value that indicates how much of the data is complete. It must be between 0 and 1, where 0 means no data is complete and 1 means all data is complete", v: 1 }, aggregation: { d: "is a number value that indicates how the data should be aggregated. It must be between 0 and 1, where 0 means no aggregation and 1 means full aggregation", v: 1 } }, h: "Unimplemented" }, "FORECAST.ETS.STAT": { s: "Returns the requested statistic for the forecast", p: { values: { d: "is the array or range of numberic data you're predicting" }, timeline: { d: "is the independent array or range of numberic data. The dtes in the timeline must have a consistent step between them and can't be zero." }, statisticType: { d: "is a number between 1 and 8 that indicates the type of statistic to return:" }, seasonality: { d: "is a number value that indicates the length of the seasonal pattern in the data. It must be between 0 and 1, where 0 means no seasonality and 1 means full seasonality", v: 1 }, dataCompletion: { d: "is a number value that indicates how much of the data is complete. It must be between 0 and 1, where 0 means no data is complete and 1 means all data is complete", v: 1 }, aggregation: { d: "is a number value that indicates how the data should be aggregated. It must be between 0 and 1, where 0 means no aggregation and 1 means full aggregation", v: 1 } }, h: "Unimplemented" }, "FORECAST.LINEAR": { s: "Calculates, or predicts, a future value along a linear trend by using existing values", p: { x: { d: "is the data point for which you want to predict a value and must be a numeric value" }, knownYs: { d: "is the dependent array or range of numeric data" }, knownXs: { d: "is the independent array or range of numeric data. The variance of Known_x's must not be zero", v: null } }, h: "Unimplemented" }, FREQUENCY: { s: "Calculates how often values occur within a range of values and then returns a vertical array of numbers having one more element than Bins_array", p: { dataArray: { d: "is an array of or reference to a set of values for which you want to count frequencies (blanks and text are ignored)" }, binsArray: { d: "is an array of or reference to intervals into which you want to group the values in data_array" } } }, GAMMA: { s: "Returns the Gamma function value", p: { x: { d: "is the value for which you want to calculate Gamma" } } }, "GAMMA.DIST": { s: "Returns the gamma distribution", p: { x: { d: "is the value at which you want to evaluate the distribution, a nonnegative number" }, alpha: { d: "is a parameter to the distribution, a positive number" }, beta: { d: "is a parameter to the distribution, a positive number. If beta = 1, GAMMA.DIST returns the standard gamma distribution" }, cumulative: { d: "is a logical value: return the cumulative distribution function = TRUE; return the probability mass function = FALSE", v: false } } }, "GAMMA.INV": { s: "Returns the inverse of the gamma cumulative distribution: if p = GAMMA.DIST(x,...), then GAMMA.INV(p,...) = x", p: { probability: { d: "is the probability associated with the gamma distribution, a number between 0 and 1, inclusive" }, alpha: { d: "is a parameter to the distribution, a positive number" }, beta: { d: "is a parameter to the distribution, a positive number. If beta = 1, GAMMA.DIST returns the standard gamma distribution" } } }, GAMMALN: { s: "Returns the natural logarithm of the gamma function", p: { x: { d: "is the value for which you want to calculate GAMMALN, a positive number" } } }, "GAMMALN.PRECISE": { s: "Returns the natural logarithm of the gamma function", p: { x: { d: "is the value for which you want to calculate GAMMALN.PRECISE, a positive number" } } }, GAUSS: { s: "Returns 0.5 less than the standard normal cumulative distribution", p: { x: { d: "is the value for which you want the distribution" } } }, GEOMEAN: { s: "Returns the geometric mean of an array or range of positive numeric data", p: { number: { d: "number1,number2,... are 1 to 255 numbers or names, arrays, or references that contain numbers for which you want the mean" } } }, GROWTH: { s: "Returns numbers in an exponential growth trend matching known data points", p: { knownYs: { d: "is the set of y-values you already know in the relationship y = b*m^x, an array or range of positive numbers" }, knownXs: { d: "is an optional set of x-values that you may already know in the relationship y = b*m^x, an array or range the same size as Known_y's", v: null }, newXs: { d: "are new x-values for which you want GROWTH to return corresponding y-values", v: null }, useConst: { d: "is a logical value: the constant b is calculated normally if Const = TRUE; b is set equal to 1 if Const = FALSE", v: true } } }, HARMEAN: { s: "Returns the harmonic mean of a data set of positive numbers: the reciprocal of the arithmetic mean of reciprocals", p: { number: { d: "number1,number2,... are 1 to 255 numbers or names, arrays, or references that contain numbers for which you want the harmonic mean" } } }, "HYPGEOM.DIST": { s: "Returns the hypergeometric distribution", p: { sampleS: { d: "is the number of successes in the sample" }, numberSample: { d: "is the size of the sample" }, populationS: { d: "is the number of successes in the population" }, numberPop: { d: "is the population size" }, cumulative: { d: "is a logical value: for the cumulative distribution function, use TRUE; for the probability density function, use FALSE" } } }, INTERCEPT: { s: "Calculates the point at which a line will intersect the y-axis by using a best-fit regression line plotted through the known x-values and y-values", p: { knownYs: { d: "is the dependent set of observations or data and can be numbers or names, arrays, or references that contain numbers" }, knownXs: { d: "is the independent set of observations or data and can be numbers or names, arrays, or references that contain numbers" } } }, KURT: { s: "Returns the kurtosis of a data set", p: { number: { d: "number1,number2,... are 1 to 255 numbers or names, arrays, or references that contain numbers for which you want the kurtosis" } } }, LINEST: { s: "Returns statistics that describe a linear trend matching known data points, by fitting a straight line using the least squares method", p: { knownYs: { d: "is the set of y-values you already know in the relationship y = mx + b" }, knownXs: { d: "is an optional set of x-values that you may already know in the relationship y = mx + b", v: null }, constant: { d: "is a logical value: the constant b is calculated normally if Const = TRUE; b is set equal to 0 if Const = FALSE", v: true }, stats: { d: "is a logical value: return additional regression statistics = TRUE; return m-coefficients and the constant b = FALSE", v: false } } }, LOGEST: { s: "Returns statistics that describe an exponential curve matching known data points", p: { knownYs: { d: "is the set of y-values you already know in the relationship y = b*m^x" }, knownXs: { d: "is an optional set of x-values that you may already know in the relationship y = b*m^x", v: null }, constant: { d: "is a logical value: the constant b is calculated normally if Const = TRUE; b is set equal to 1 if Const = FALSE", v: true }, stats: { d: "is a logical value: return additional regression statistics = TRUE; return m-coefficients and the constant b = FALSE", v: false } }, h: "Unimplemented Buggy" }, "LOGNORM.DIST": { s: "Returns the lognormal distribution of x, where ln(x) is normally distributed with parameters Mean and Standard_dev", p: { x: { d: "is the value at which to evaluate the function, a positive number" }, mean: { d: "is the mean of ln(x)" }, standardDev: { d: "is the standard deviation of ln(x), a positive number" }, cumulative: { d: "is a logical value: for the cumulative distribution function, use TRUE; for the probability density function, use FALSE" } } }, "LOGNORM.INV": { s: "Returns the inverse of the lognormal cumulative distribution function of x, where ln(x) is normally distributed with parameters Mean and Standard_dev", p: { probability: { d: "is a probability associated with the lognormal distribution, a number between 0 and 1, inclusive" }, mean: { d: "is the mean of ln(x)" }, standardDev: { d: "is the standard deviation of ln(x), a positive number" } } }, "MODE.MULT": { s: "Returns a vertical array of the most frequently occurring, or repetitive, values in an array or range of data.  For a horizontal array, use =TRANSPOSE(MODE.MULT(number1,number2,...))", p: { number: { d: '<div class="param-538">number1,number2,... are 1 to 255 numbers, or names, arrays, or references that contain numbers for which you want the mode</div>' } }, h: "Unimplemented" }, "MODE.SNGL": { s: "Returns the most frequently occurring, or repetitive, value in an array or range of data", p: { number: { d: "number1,number2,... are 1 to 255 numbers, or names, arrays, or references that contain numbers for which you want the mode" } }, h: "Unimplemented" }, "NEGBINOM.DIST": { s: "Returns 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", p: { numberF: { d: "is the number of failures" }, numberS: { d: "is the threshold number of successes" }, probabilityS: { d: "is the probability of a success; a number between 0 and 1" }, cumulative: { d: "is a logical value: for the cumulative distribution function, use TRUE; for the probability mass function, use FALSE" } } }, "NORM.DIST": { s: "Returns the normal distribution for the specified mean and standard deviation", p: { x: { d: "is the value for which you want the distribution" }, mean: { d: "is the arithmetic mean of the distribution" }, standardDev: { d: "is the standard deviation of the distribution, a positive number" }, cumulative: { d: "is a logical value: for the cumulative distribution function, use TRUE; for the probability density function, use FALSE" } } }, "NORM.INV": { s: "Returns the inverse of the normal cumulative distribution for the specified mean and standard deviation", p: { probability: { d: "is a probability corresponding to the normal distribution, a number between 0 and 1 inclusive" }, mean: { d: "is the arithmetic mean of the distribution" }, standardDev: { d: "is the standard deviation of the distribution, a positive number" } } }, "NORM.S.DIST": { s: "Returns the standard normal distribution (has a mean of zero and a standard deviation of one)", p: { z: { d: "is the value for which you want the distribution" }, cumulative: { d: "is a logical value for the function to return: the cumulative distribution function = TRUE; the probability density function = FALSE" } } }, "NORM.S.INV": { s: "Returns the inverse of the standard normal cumulative distribution (has a mean of zero and a standard deviation of one)", p: { probability: { d: "is a probability corresponding to the normal distribution, a number between 0 and 1 inclusive" } } }, PEARSON: { s: "Returns the Pearson product moment correlation coefficient, r", p: { array1: { d: "is a set of independent values" }, array2: { d: "is a set of dependent values" } } }, "PERCENTILE.EXC": { s: "Returns the k-th percentile of values in a range, where k is in the range 0..1, exclusive", p: { array: { d: "is the array or range of data that defines relative standing" }, k: { d: "is the percentile value that is between 0 through 1, inclusive" } }, h: "Unimplemented" }, "PERCENTILE.INC": { s: "Returns the k-th percentile of values in a range, where k is in the range 0..1, inclusive", p: { array: { d: "is the array or range of data that defines relative standing" }, k: { d: "is the percentile value that is between 0 through 1, inclusive" } }, h: "Unimplemented" }, "PERCENTRANK.EXC": { s: "Returns the rank of a value in a data set as a percentage (0..1, exclusive) of the data set", p: { array: { d: "is the array or range of data with numeric values that defines relative standing" }, x: { d: "is the value for which you want to know the rank" }, significance: { d: "is an optional value that identifies the number of significant digits for the returned percentage", v: 3 } }, h: "Unimplemented" }, "PERCENTRANK.INC": { s: "Returns the rank of a value in a data set as a percentage (0..1, inclusive) of the data set", p: { array: { d: "is the array or range of data with numeric values that defines relative standing" }, x: { d: "is the value for which you want to know the rank" }, significance: { d: "is an optional value that identifies the number of significant digits for the returned percentage", v: 3 } }, h: "Unimplemented" }, PHI: { s: "Returns the value of the density function for a standard normal distribution", p: { x: { d: "is the number for which you want the density of the standard normal distribution" } } }, "POISSON.DIST": { s: "Returns the Poisson distribution", p: { x: { d: "is the number of events" }, mean: { d: "is the expected numeric value, a positive number" }, cumulative: { d: "is a logical value: for the cumulative Poisson probability, use TRUE; for the Poisson probability mass function, use FALSE" } } }, PROB: { s: "Returns the probability that values in a range are between two limits or equal to a lower limit", p: { xRange: { d: "is the range of numeric values of x with which there are associated probabilities" }, probRange: { d: "is the set of probabilities associated with values in X_range, values between 0 and 1 and excluding 0" }, lowerLimit: { d: "is the lower bound on the value for which you want a probability" }, upperLimit: { d: "is the optional upper bound on the value. If omitted, PROB returns the probability that X_range values are equal to Lower_limit" } } }, "QUARTILE.EXC": { s: "Returns the quartile of a data set, based on percentile values from 0..1, exclusive", p: { array: { d: "is the array or cell range of numeric values for which you want the quartile value" }, quart: { d: "is a number: minimum value = 0; 1st quartile = 1; median value = 2; 3rd quartile = 3; maximum value = 4" } }, h: "Unimplemented" }, "QUARTILE.INC": { s: "Returns the quartile of a data set, based on percentile values from 0..1, inclusive", p: { array: { d: "is the array or cell range of numeric values for which you want the quartile value" }, quart: { d: "is a number: minimum value = 0; 1st quartile = 1; median value = 2; 3rd quartile = 3; maximum value = 4" } }, h: "Unimplemented" }, "RANK.AVG": { s: "Returns 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", p: { number: { d: "is the number for which you want to find the rank" }, ref: { d: "is an array of, or a reference to, a list of numbers. Nonnumeric values are ignored" }, order: { d: "is a number: rank in the list sorted descending = 0; rank in the list sorted ascending = any nonzero value", v: 0 } }, h: "Unimplemented" }, "RANK.EQ": { s: "Returns 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 top rank of that set of values is returned", p: { number: { d: "is the number for which you want to find the rank" }, ref: { d: "is an array of, or a reference to, a list of numbers. Nonnumeric values are ignored" }, order: { d: "is a number: rank in the list sorted descending = 0; rank in the list sorted ascending = any nonzero value", v: 0 } }, h: "Unimplemented" }, RSQ: { s: "Returns the square of the Pearson product moment correlation coefficient through the given data points", p: { knownYs: { d: "is an array or range of data points and can be numbers or names, arrays, or references that contain numbers" }, knownXs: { d: "is an array or range of data points and can be numbers or names, arrays, or references that contain numbers" } }, h: "Unimplemented" }, SKEW: { s: "Returns the skewness of a distribution: a characterization of the degree of asymmetry of a distribution around its mean", p: { number: { d: "number1,number2,... are 1 to 255 numbers or names, arrays, or references that contain numbers for which you want the skewness" } }, h: "Unimplemented" }, "SKEW.P": { s: "Returns the skewness of a distribution based on a population: a characterization of the degree of asymmetry of a distribution around its mean", p: { number: { d: "number1,number2,... are 1 to 254 numbers or names, arrays, or references that contain numbers for which you want the population skewness" } }, h: "Unimplemented" }, SLOPE: { s: "Returns the slope of the linear regression line through the given data points", p: { knownYs: { d: "is an array or cell range of numeric dependent data points and can be numbers or names, arrays, or references that contain numbers" }, knownXs: { d: "iis the set of independent data points and can be numbers or names, arrays, or references that contain numbers" } } }, STANDARDIZE: { s: "Returns a normalized value from a distribution characterized by a mean and standard deviation", p: { x: { d: "is the value you want to normalize" }, mean: { d: "is the arithmetic mean of the distribution" }, standardDev: { d: "is the standard deviation of the distribution, a positive number" } } }, "STDEV.P": { s: "Calculates standard deviation based on the entire population given as arguments (ignores logical values and text)", p: { number: { d: "number1,number2,... are 1 to 255 numbers corresponding to a population and can be numbers or references that contain numbers" } }, h: "Unimplemented" }, "STDEV.S": { s: "Estimates standard deviation based on a sample (ignores logical values and text in the sample)", p: { number: { d: "number1,number2,... are 1 to 255 numbers corresponding to a sample of a population and can be numbers or references that contain numbers" } } }, STDEVA: { s: "Estimates standard deviation based on a sample, including logical values and text. Text and the logical value FALSE have the value 0; the logical value TRUE has the value 1", p: { value: { d: "value1,value2,... are 1 to 255 values corresponding to a sample of a population and can be values or names or references to values" } }, h: "Unimplemented" }, STDEVPA: { s: "Calculates standard deviation based on an entire population, including logical values and text. Text and the logical value FALSE have the value 0; the logical value TRUE has the value 1", p: { value: { d: "value1,value2,... are 1 to 255 values corresponding to a population and can be values, names, arrays, or references that contain values" } }, h: "Unimplemented" }, STEYX: { s: "Returns the standard error of the predicted y-value for each x in a regression", p: { knownYs: { d: "is an array or range of dependent data points and can be numbers or names, arrays, or references that contain numbers" }, knownXs: { d: "is an array or range of independent data points and can be numbers or names, arrays, or references that contain numbers" } }, h: "Unimplemented" }, "T.DIST": { s: "Returns the left-tailed Student's t-distribution", p: { x: { d: "is the numeric value at which to evaluate the distribution" }, degFreedom: { d: "is an integer indicating the number of degrees of freedom that characterize the distribution" }, cumulative: { d: "is a logical value: for the cumulative distribution function, use TRUE; for the probability density function, use FALSE" } } }, "T.DIST.2T": { s: "Returns the two-tailed Student's t-distribution", p: { x: { d: "is the numeric value at which to evaluate the distribution" }, degFreedom: { d: "is an integer indicating the number of degrees of freedom that characterize the distribution" } } }, "T.DIST.RT": { s: "Returns the right-tailed Student's t-distribution", p: { x: { d: "is the numeric value at which to evaluate the distribution" }, degFreedom: { d: "is an integer indicating the number of degrees of freedom that characterize the distribution" } } }, "T.INV": { s: "Returns the left-tailed inverse of the Student's t-distribution", p: { probability: { d: "is the probability associated with the two-tailed Student's t-distribution, a number between 0 and 1 inclusive" }, degFreedom: { d: "is a positive integer indicating the number of degrees of freedom to characterize the distribution" } } }, "T.INV.2T": { s: "Returns the two-tailed inverse of the Student's t-distribution", p: { probability: { d: "is the probability associated with the two-tailed Student's t-distribution, a number between 0 and 1 inclusive" }, degFreedom: { d: "is a positive integer indicating the number of degrees of freedom to characterize the distribution" } } }, "T.TEST": { s: "Returns the probability associated with a Student's t-Test", p: { array1: { d: "is the first data set" }, array2: { d: "is the second data set" }, tails: { d: "specifies the number of distribution tails to return: one-tailed distribution = 1; two-tailed distribution = 2" }, type: { d: "is the kind of t-test: paired = 1, two-sample equal variance (homoscedastic) = 2, two-sample unequal variance = 3" } }, h: "Unimplemented" }, TREND: { s: "Returns numbers in a linear trend matching known data points, using the least squares method", p: { knownYs: { d: "is a range or array of y-values you already know in the relationship y = mx + b" }, knownXs: { d: "is an optional range or array of x-values that you know in the relationship y = mx + b, an array the same size as Known_y's", v: null }, newXs: { d: "is a range or array of new x-values for which you want TREND to return corresponding y-values", v: null }, consts: { d: "is a logical value: the constant b is calculated normally if Const = TRUE; b is set equal to 0 if Const = FALSE", v: true } }, h: "Unimplemented" }, TRIMMEAN: { s: "Returns the mean of the interior portion of a set of data values", p: { array: { d: "is the range or array of values to trim and average" }, percent: { d: "is the fractional number of data points to exclude from the top and bottom of the data set" } }, h: "Unimplemented" }, "VAR.P": { s: "Calculates variance based on the entire population (ignores logical values and text in the population)", p: { number: { d: "number1,number2,... are 1 to 255 numeric arguments corresponding to a population" } } }, "VAR.S": { s: "Estimates variance based on a sample (ignores logical values and text in the sample)", p: { number: { d: "number1,number2,... are 1 to 255 numeric arguments corresponding to a sample of a population" } } }, VARA: { s: "Estimates variance based on a sample, including logical values and text. Text and the logical value FALSE have the value 0; the logical value TRUE has the value 1", p: { value: { d: "value1,value2,... are 1 to 255 value arguments corresponding to a sample of a population" } } }, VARPA: { s: "Calculates variance based on the entire population, including logical values and text. Text and the logical value FALSE have the value 0; the logical value TRUE has the value 1", p: { value: { d: "value1,value2,... are 1 to 255 value arguments corresponding to a population" } } }, "WEIBULL.DIST": { s: "Returns the Weibull distribution", p: { x: { d: "is the value at which to evaluate the function, a nonnegative number" }, alpha: { d: "is a parameter to the distribution, a positive number" }, beta: { d: "is a parameter to the distribution, a positive number" }, cumulative: { d: "is a logical value: for the cumulative distribution function, use TRUE; for the probability mass function, use FALSE" } } }, "Z.TEST": { s: "Returns the one-tailed P-value of a z-test", p: { array: { d: "is the array or range of data against which to test X" }, x: { d: "is the value to test" }, sigma: { d: "is the population (known) standard deviation. If omitted, the sample standard deviation is used", v: 0 } } } };
export {
  e as default
};