Expression Editor Functions - Statistical Category

Returns the average of the absolute mean deviations of data points. AveDev is a measure of the variability in a data set.

Format: AveDev(number1, number2, ...)

Returns the average (arithmetic mean) of the arguments.

Format: Average(number1, number2, ...)

Returns the average (arithmetic mean) of the arguments. In addition to numbers and text, logical values such as True and False can also be included.

Format: AverageA(value1, value2, ...)

Returns the average (arithmetic mean) of the arguments.

Format: Avg(number1, number2, ...)

Returns the individual term binomial distribution probability.

Format: BinomDist(number, trials, probability, cumulative)

Returns the one-tailed probability of the chi-squared distribution. The chi distribution is associated with a chi test.

Format: ChiDist(x, degrees_freedom)

Returns the inverse of the one-tailed probability of the chi-squared distribution. If probability = CHIDIST(x,...), then CHIINV(probability,...) = x. Use this function to compare observed results with expected ones to decide whether your original hypothesis is valid.

Format: ChiInv(probability, degrees_freedom)

Returns a value to construct a confidence interval about a population mean. The confidence interval is a range of values. In a sample, mean x is at the centre of this range and the range is x » Confidence. For example, if x is the sample mean of delivery times for products ordered through the mail, x » Confidence is a range of population means.

Format: Confidence(alpha, standard_dev, size)

Counts the number of items in a list that contains numbers.

Format: Count(value1, value2,...)

Counts the number of cells that are not empty.

Format: CountA(value1, value2,...)

Returns the smallest value for which, the cumulative binomial distribution is greater than or equal to a criterion value.

Format: CritBinom(trials, probability_s, alpha)

Returns the sum of squares of deviations of data points from their sample mean.

Format: DevSq(number1, number2, ...)

Returns the exponential distribution.

Format: ExponDist(x, lambda, cumulative)

Returns the Fisher transformation.

Format: Fisher(x)

Returns the inverse of the Fisher transformation.

Format: FisherInv(y)

Returns the gamma distribution.

Format: GammaDist(x, alpha, beta, cumulative)

Returns the inverse of the gamma distribution.

Format: GammaInv(p, alpha, beta)

Returns the natural logarithm of the gamma distribution.

Format: GammaLn(value)

Returns the geometric mean of an array or range of positive data.

Format: GeoMean(number1, number2, ...)

Returns the harmonic mean of a data set. The harmonic mean is the reciprocal of the arithmetic mean of reciprocals.

Format: HarMean(number1, number2, ...)

Returns the hypergeometric distribution.

Format: HypGeomDist(sample, n_sample, population, n_population)

Returns the kurtosis of a data set. Kurtosis characterises the relative peakedness or flatness of a distribution compared with the normal distribution. Positive kurtosis indicates a relatively peaked distribution. Negative kurtosis indicates a relatively flat distribution.

Format: KURT(number1, number2, ...)

Returns the inverse of the lognormal cumulative distribution function of x, where ln(x) is normally distributed with parameters mean and standard_dev. If p = LogNormDist(x,...), then LogInv(p,...) = x.

Format: LogInv(probability, mean, standard_dev)

Returns the cumulative lognormal distribution of x.

Format: LogNormDist(x, mean, standard_dev)

Returns the maximum of specified values separated by commas.

Format: Max(x)

Returns the largest value in a list of arguments. Text and logical values such as True and False are compared as well as numbers.

Format: MaxA(value1, value2, ...)

Returns the median of the given numbers. The median is the number in the middle of a set of numbers; that is, half the numbers have values that are greater than the median and half have values that are less.

Format: Median(number1, number2, ...)

Returns the minimum of specified values separated by commas.

Format: Min(x)

Returns the smallest value in the list of arguments. Text and logical values such as True and False are compared as well as numbers.

Format: MinA(value1, value2, ...)

Returns the most frequently occurring or repetitive value in an array or range of data.

Format: Mode(number1, number2, ...)

Returns the negative binomial distribution.

Format: NegBinomDist(number_f, number_s, probability_s)

Returns the normal cumulative distribution.

Format: NormDist(x, mean, stdev)

Returns the inverse of the normal cumulative distribution for the specified mean and standard deviation.

Format: NormInv(probability, mean, standard_dev)

Returns the standard normal cumulative distribution. The distribution has a mean of zero and a standard deviation of one.

Format: NormSDist(distribution_value)

Returns the inverse of the standard normal cumulative distribution. The distribution has a mean of zero and a standard deviation of one.

Format: NormSInv(probability)

Returns the Poisson distribution.

Format: Poisson(x, mean, cumulative)

Returns the quartile of a data set.

Format: Quartile(array, quart)

Returns the skewness of a distribution. Skewness characterises the degree of asymmetry of a distribution around its mean.

Format: Skew(number1, number2, ...)

Returns a normalised value.

Format: Standardize(x, mean, stdev)

Estimates the standard deviation based on a sample. The standard deviation is a measure of how widely values are dispersed from the average value (the mean).

Format: StDev(number1, number2, ...)

Estimates standard deviation based on a sample. The standard deviation is a measure of how widely values are dispersed from the average value (the mean). Text and logical values such as True and False are also included in the calculation.

Format: StDevA(value1, value , ...)

Calculates standard deviation based on the entire population given as arguments.

Format: StDevP(number1, number2, ...)

Calculates the standard deviation based on the entire population given as arguments, including text and logical values.

Format: StDevPA(value1, value2, ...)

Returns the variance of a population based on sample of numbers.

Format: Var(number1, number2, ... number_n)

Returns the variance of a population based on a sample of numbers, text, and logical values such as True or False.

Format: VarA(value1, value2, ... value_n)

Calculates variance based on the entire population. In addition to numbers and text, logical values such as True and False are also included in the calculation.

Format: VarPA(value1, value2, ...)