Statistics

  • ADTEST Anderson-Darling Test of Normality
  • AVEDEV average of the absolute deviations of a data set
  • AVERAGE average of all the numeric values and cells
  • AVERAGEA average of all the values and cells
  • BERNOULLI probability mass function of a Bernoulli distribution
  • BETA.DIST cumulative distribution function of the beta distribution
  • BETADIST cumulative distribution function of the beta distribution
  • BETAINV inverse of the cumulative distribution function of the beta distribution
  • BINOM.DIST.RANGE probability of the binomial distribution over an interval
  • BINOMDIST probability mass or cumulative distribution function of the binomial distribution
  • CAUCHY probability density or cumulative distribution function of the Cauchy, Lorentz or Breit-Wigner distribution
  • CHIDIST survival function of the chi-squared distribution
  • CHIINV inverse of the survival function of the chi-squared distribution
  • CHITEST p value of the Goodness of Fit Test
  • CONFIDENCE margin of error of a confidence interval for the population mean
  • CONFIDENCE.T margin of error of a confidence interval for the population mean using the Student's t-distribution
  • CORREL Pearson correlation coefficient of two data sets
  • COUNT total number of integer or floating point arguments passed
  • COUNTA number of arguments passed not including empty cells
  • COVAR covariance of two data sets
  • COVARIANCE.S sample covariance of two data sets
  • CRITBINOM right-tailed critical value of the binomial distribution
  • CRONBACH Cronbach's alpha
  • CVMTEST Cramér-von Mises Test of Normality
  • DEVSQ sum of squares of deviations of a data set
  • EXPONDIST probability density or cumulative distribution function of the exponential distribution
  • EXPPOWDIST the probability density function of the Exponential Power distribution
  • FDIST survival function of the F distribution
  • FINV inverse of the survival function of the F distribution
  • FISHER Fisher transformation
  • FISHERINV inverse of the Fisher transformation
  • FORECAST estimates a future value according to existing values using simple linear regression
  • FREQUENCY frequency table
  • FTEST p-value for the two-tailed hypothesis test comparing the variances of two populations
  • GAMMADIST probability density or cumulative distribution function of the gamma distribution
  • GAMMAINV inverse of the cumulative gamma distribution
  • GEOMDIST probability mass or cumulative distribution function of the geometric distribution
  • GEOMEAN geometric mean
  • GROWTH exponential growth prediction
  • HARMEAN harmonic mean
  • HYPGEOMDIST probability mass or cumulative distribution function of the hypergeometric distribution
  • INTERCEPT the intercept of a linear regression line
  • KURT unbiased estimate of the kurtosis of a data set
  • KURTP population kurtosis of a data set
  • LANDAU approximate probability density function of the Landau distribution
  • LAPLACE probability density function of the Laplace distribution
  • LARGE k-th largest value in a data set
  • LEVERAGE calculate regression leverage
  • LINEST multiple linear regression coefficients and statistics
  • LKSTEST Lilliefors (Kolmogorov-Smirnov) Test of Normality
  • LOGEST exponential least square fit
  • LOGFIT logarithmic least square fit (using a trial and error method)
  • LOGINV inverse of the cumulative distribution function of the lognormal distribution
  • LOGISTIC probability density function of the logistic distribution
  • LOGNORMDIST cumulative distribution function of the lognormal distribution
  • LOGREG the logarithmic regression
  • MAX largest value, with negative numbers considered smaller than positive numbers
  • MAXA largest value, with negative numbers considered smaller than positive numbers
  • MEDIAN median of a data set
  • MIN smallest value, with negative numbers considered smaller than positive numbers
  • MINA smallest value, with negative numbers considered smaller than positive numbers
  • MODE first most common number in the dataset
  • MODE.MULT most common numbers in the dataset
  • NEGBINOMDIST probability mass function of the negative binomial distribution
  • NORMDIST probability density or cumulative distribution function of a normal distribution
  • NORMINV inverse of the cumulative distribution function of a normal distribution
  • NORMSDIST cumulative distribution function of the standard normal distribution
  • NORMSINV inverse of the cumulative distribution function of the standard normal distribution
  • OWENT Owen's T function
  • PARETO probability density function of the Pareto distribution
  • PEARSON Pearson correlation coefficient of the paired set of data
  • PERCENTILE determines the 100*k-th percentile of the given data points (Hyndman-Fan method 7: N-1 basis)
  • PERCENTILE.EXC determines the 100*k-th percentile of the given data points (Hyndman-Fan method 6: N+1 basis)
  • PERCENTRANK rank of a data point in a data set (Hyndman-Fan method 7: N-1 basis)
  • PERCENTRANK.EXC rank of a data point in a data set (Hyndman-Fan method 6: N+1 basis)
  • PERMUT number of k-permutations of a n-set
  • PERMUTATIONA the number of permutations of y objects chosen from x objects with repetition allowed
  • POISSON probability mass or cumulative distribution function of the Poisson distribution
  • PROB probability of an interval for a discrete (and finite) probability distribution
  • QUARTILE the k-th quartile of the data points (Hyndman-Fan method 7: N-1 basis)
  • QUARTILE.EXC the k-th quartile of the data points (Hyndman-Fan method 6: N+1 basis)
  • R.DBETA probability density function of the beta distribution
  • R.DBINOM probability density function of the binomial distribution
  • R.DCAUCHY probability density function of the Cauchy distribution
  • R.DCHISQ probability density function of the chi-square distribution
  • R.DEXP probability density function of the exponential distribution
  • R.DF probability density function of the F distribution
  • R.DGAMMA probability density function of the gamma distribution
  • R.DGEOM probability density function of the geometric distribution
  • R.DGUMBEL probability density function of the Gumbel distribution
  • R.DHYPER probability density function of the hypergeometric distribution
  • R.DLNORM probability density function of the log-normal distribution
  • R.DNBINOM probability density function of the negative binomial distribution
  • R.DNORM probability density function of the normal distribution
  • R.DPOIS probability density function of the Poisson distribution
  • R.DRAYLEIGH probability density function of the Rayleigh distribution
  • R.DSNORM probability density function of the skew-normal distribution
  • R.DST probability density function of the skew-t distribution
  • R.DT probability density function of the Student t distribution
  • R.DWEIBULL probability density function of the Weibull distribution
  • R.PBETA cumulative distribution function of the beta distribution
  • R.PBINOM cumulative distribution function of the binomial distribution
  • R.PCAUCHY cumulative distribution function of the Cauchy distribution
  • R.PCHISQ cumulative distribution function of the chi-square distribution
  • R.PEXP cumulative distribution function of the exponential distribution
  • R.PF cumulative distribution function of the F distribution
  • R.PGAMMA cumulative distribution function of the gamma distribution
  • R.PGEOM cumulative distribution function of the geometric distribution
  • R.PGUMBEL cumulative distribution function of the Gumbel distribution
  • R.PHYPER cumulative distribution function of the hypergeometric distribution
  • R.PLNORM cumulative distribution function of the log-normal distribution
  • R.PNBINOM cumulative distribution function of the negative binomial distribution
  • R.PNORM cumulative distribution function of the normal distribution
  • R.PPOIS cumulative distribution function of the Poisson distribution
  • R.PRAYLEIGH cumulative distribution function of the Rayleigh distribution
  • R.PSNORM cumulative distribution function of the skew-normal distribution
  • R.PST cumulative distribution function of the skew-t distribution
  • R.PT cumulative distribution function of the Student t distribution
  • R.PTUKEY cumulative distribution function of the Studentized range distribution
  • R.PWEIBULL cumulative distribution function of the Weibull distribution
  • R.QBETA probability quantile function of the beta distribution
  • R.QBINOM probability quantile function of the binomial distribution
  • R.QCAUCHY probability quantile function of the Cauchy distribution
  • R.QCHISQ probability quantile function of the chi-square distribution
  • R.QEXP probability quantile function of the exponential distribution
  • R.QF probability quantile function of the F distribution
  • R.QGAMMA probability quantile function of the gamma distribution
  • R.QGEOM probability quantile function of the geometric distribution
  • R.QGUMBEL probability quantile function of the Gumbel distribution
  • R.QHYPER probability quantile function of the hypergeometric distribution
  • R.QLNORM probability quantile function of the log-normal distribution
  • R.QNBINOM probability quantile function of the negative binomial distribution
  • R.QNORM probability quantile function of the normal distribution
  • R.QPOIS probability quantile function of the Poisson distribution
  • R.QRAYLEIGH probability quantile function of the Rayleigh distribution
  • R.QSNORM probability quantile function of the skew-normal distribution
  • R.QST probability quantile function of the skew-t distribution
  • R.QT probability quantile function of the Student t distribution
  • R.QTUKEY probability quantile function of the Studentized range distribution
  • R.QWEIBULL probability quantile function of the Weibull distribution
  • RANK rank of a number in a list of numbers
  • RANK.AVG rank of a number in a list of numbers
  • RAYLEIGH probability density function of the Rayleigh distribution
  • RAYLEIGHTAIL probability density function of the Rayleigh tail distribution
  • RSQ square of the Pearson correlation coefficient of the paired set of data
  • SFTEST Shapiro-Francia Test of Normality
  • SKEW unbiased estimate for skewness of a distribution
  • SKEWP population skewness of a data set
  • SLOPE the slope of a linear regression line
  • SMALL k-th smallest value in a data set
  • SNORM.DIST.RANGE probability of the standard normal distribution over an interval
  • SSMEDIAN median for grouped data
  • STANDARDIZE z-score of a value
  • STDEV sample standard deviation of the given sample
  • STDEVA sample standard deviation of the given sample
  • STDEVP population standard deviation of the given population
  • STDEVPA population standard deviation of an entire population
  • STEYX standard error of the predicted y-value in the regression
  • SUBTOTAL the subtotal of the given list of arguments
  • TDIST survival function of the Student t-distribution
  • TINV two tailed inverse of the Student t-distribution
  • TREND estimates future values of a given data set using a least squares approximation
  • TRIMMEAN mean of the interior of a data set
  • TTEST p-value for a hypothesis test comparing the means of two populations using the Student t-distribution
  • VAR sample variance of the given sample
  • VARA sample variance of the given sample
  • VARP variance of an entire population
  • VARPA variance of an entire population
  • WEIBULL probability density or cumulative distribution function of the Weibull distribution
  • ZTEST the probability of observing a sample mean as large as or larger than the mean of the given sample

ADTEST

ADTEST Anderson-Darling Test of Normality

Synopsis

ADTEST(x)

Arguments

x: array of sample values

Description

This function returns an array with the first row giving the p-value of the Anderson-Darling Test, the second row the test statistic of the test, and the third the number of observations in the sample.

Note

If there are less than 8 sample values, ADTEST returns #VALUE!

See also

CHITEST, CVMTEST, LKSTEST, SFTEST.

AVEDEV

AVEDEV average of the absolute deviations of a data set

Synopsis

AVEDEV(number1,number2,…)

Arguments

number1: first value

number2: second value

Microsoft Excel Compatibility

This function is Excel compatible.

See also

STDEV.

AVERAGE

AVERAGE average of all the numeric values and cells

Synopsis

AVERAGE(number1,number2,…)

Arguments

number1: first value

number2: second value

Microsoft Excel Compatibility

This function is Excel compatible.

See also

SUM, COUNT.

AVERAGEA

AVERAGEA average of all the values and cells

Synopsis

AVERAGEA(number1,number2,…)

Arguments

number1: first value

number2: second value

Description

Numbers, text and logical values are included in the calculation too. If the cell contains text or the argument evaluates to FALSE, it is counted as value zero (0). If the argument evaluates to TRUE, it is counted as one (1). Note that empty cells are not counted.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

AVERAGE.

BERNOULLI

BERNOULLI probability mass function of a Bernoulli distribution

Synopsis

BERNOULLI(k,p)

Arguments

k: integer

p: probability of success

Note

If k != 0 and k != 1 this function returns a #NUM! error. If p < 0 or p > 1 this function returns a #NUM! error.

See also

RANDBERNOULLI.

BETA.DIST

BETA.DIST cumulative distribution function of the beta distribution

Synopsis

BETA.DIST(x,alpha,beta,cumulative,a,b)

Arguments

x: number

alpha: scale parameter

beta: scale parameter

cumulative: whether to evaluate the density function or the cumulative distribution function

a: optional lower bound, defaults to 0

b: optional upper bound, defaults to 1

Note

If x < a or x > b this function returns a #NUM! error. If alpha <= 0 or beta <= 0, this function returns a #NUM! error. If a >= b this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

BETAINV, BETADIST.

BETADIST

BETADIST cumulative distribution function of the beta distribution

Synopsis

BETADIST(x,alpha,beta,a,b)

Arguments

x: number

alpha: scale parameter

beta: scale parameter

a: optional lower bound, defaults to 0

b: optional upper bound, defaults to 1

Note

If x < a or x > b this function returns a #NUM! error. If alpha <= 0 or beta <= 0, this function returns a #NUM! error. If a >= b this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

BETAINV, BETA.DIST.

BETAINV

BETAINV inverse of the cumulative distribution function of the beta distribution

Synopsis

BETAINV(p,alpha,beta,a,b)

Arguments

p: probability

alpha: scale parameter

beta: scale parameter

a: optional lower bound, defaults to 0

b: optional upper bound, defaults to 1

Note

If p < 0 or p > 1 this function returns a #NUM! error. If alpha <= 0 or beta <= 0, this function returns a #NUM! error. If a >= b this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

BETADIST, BETA.DIST.

BINOM.DIST.RANGE

BINOM.DIST.RANGE probability of the binomial distribution over an interval

Synopsis

BINOM.DIST.RANGE(trials,p,start,end)

Arguments

trials: number of trials

p: probability of success in each trial

start: start of the interval

end: end of the interval, defaults to start

Note

If start, end or trials are non-integer they are truncated. If trials < 0 this function returns a #NUM! error. If p < 0 or p > 1 this function returns a #NUM! error. If start > end this function returns 0.

OpenDocument Format (ODF) Compatibility

This function is OpenFormula compatible.

See also

BINOMDIST, R.PBINOM.

BINOMDIST

BINOMDIST probability mass or cumulative distribution function of the binomial distribution

Synopsis

BINOMDIST(n,trials,p,cumulative)

Arguments

n: number of successes

trials: number of trials

p: probability of success in each trial

cumulative: whether to evaluate the mass function or the cumulative distribution function

Note

If n or trials are non-integer they are truncated. If n < 0 or trials < 0 this function returns a #NUM! error. If n > trials this function returns a #NUM! error. If p < 0 or p > 1 this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

POISSON.

CAUCHY

CAUCHY probability density or cumulative distribution function of the Cauchy, Lorentz or Breit-Wigner distribution

Synopsis

CAUCHY(x,a,cumulative)

Arguments

x: number

a: scale parameter

cumulative: whether to evaluate the density function or the cumulative distribution function

Note

If a < 0 this function returns a #NUM! error. If cumulative is neither TRUE nor FALSE this function returns a #VALUE! error.

See also

RANDCAUCHY.

CHIDIST

CHIDIST survival function of the chi-squared distribution

Synopsis

CHIDIST(x,dof)

Arguments

x: number

dof: number of degrees of freedom

Description

The survival function is 1 minus the cumulative distribution function.

Note

If dof is non-integer it is truncated. If dof < 1 this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

OpenDocument Format (ODF) Compatibility

CHIDIST(x,dof) is the OpenFormula function LEGACY.CHIDIST(x,dof).

See also

CHIINV, CHITEST.

CHIINV

CHIINV inverse of the survival function of the chi-squared distribution

Synopsis

CHIINV(p,dof)

Arguments

p: probability

dof: number of degrees of freedom

Description

The survival function is 1 minus the cumulative distribution function.

Note

If p < 0 or p > 1 or dof < 1 this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

OpenDocument Format (ODF) Compatibility

CHIINV(p,dof) is the OpenFormula function LEGACY.CHIDIST(p,dof).

See also

CHIDIST, CHITEST.

CHITEST

CHITEST p value of the Goodness of Fit Test

Synopsis

CHITEST(actual_range,theoretical_range)

Arguments

actual_range: observed data

theoretical_range: expected values

Note

If the actual range is not an n by 1 or 1 by n range, but an n by m range, then CHITEST uses (n-1) times (m-1) as degrees of freedom. This is useful if the expected values were calculated from the observed value in a test of independence or test of homogeneity.

Microsoft Excel Compatibility

This function is Excel compatible.

OpenDocument Format (ODF) Compatibility

CHITEST is the OpenFormula function LEGACY.CHITEST.

See also

CHIDIST, CHIINV.

CONFIDENCE

CONFIDENCE margin of error of a confidence interval for the population mean

Synopsis

CONFIDENCE(alpha,stddev,size)

Arguments

alpha: significance level

stddev: population standard deviation

size: sample size

Note

This function requires the usually unknown population standard deviation. If size is non-integer it is truncated. If size < 0 this function returns a #NUM! error. If size is 0 this function returns a #DIV/0! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

AVERAGE, CONFIDENCE.T.

CONFIDENCE.T

CONFIDENCE.T margin of error of a confidence interval for the population mean using the Student's t-distribution

Synopsis

CONFIDENCE.T(alpha,stddev,size)

Arguments

alpha: significance level

stddev: sample standard deviation

size: sample size

Note

If stddev < 0 or = 0 this function returns a #NUM! error. If size is non-integer it is truncated. If size < 1 this function returns a #NUM! error. If size is 1 this function returns a #DIV/0! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

AVERAGE, CONFIDENCE.

CORREL

CORREL Pearson correlation coefficient of two data sets

Synopsis

CORREL(array1,array2)

Arguments

array1: first data set

array2: second data set

Description

Strings and empty cells are simply ignored.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

COVAR, FISHER, FISHERINV.

COUNT

COUNT total number of integer or floating point arguments passed

Synopsis

COUNT(number1,number2,…)

Arguments

number1: first value

number2: second value

Microsoft Excel Compatibility

This function is Excel compatible.

See also

AVERAGE.

COUNTA

COUNTA number of arguments passed not including empty cells

Synopsis

COUNTA(number1,number2,…)

Arguments

number1: first value

number2: second value

Microsoft Excel Compatibility

This function is Excel compatible.

COVAR

COVAR covariance of two data sets

Synopsis

COVAR(array1,array2)

Arguments

array1: first data set

array2: set data set

Description

Strings and empty cells are simply ignored.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

CORREL, FISHER, FISHERINV.

COVARIANCE.S

COVARIANCE.S sample covariance of two data sets

Synopsis

COVARIANCE.S(array1,array2)

Arguments

array1: first data set

array2: set data set

Description

Strings and empty cells are simply ignored.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

COVAR, CORREL.

CRITBINOM

CRITBINOM right-tailed critical value of the binomial distribution

Synopsis

CRITBINOM(trials,p,alpha)

Arguments

trials: number of trials

p: probability of success in each trial

alpha: significance level (area of the tail)

Note

If trials is a non-integer it is truncated. If trials < 0 this function returns a #NUM! error. If p < 0 or p > 1 this function returns a #NUM! error. If alpha < 0 or alpha > 1 this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

BINOMDIST.

CRONBACH

CRONBACH Cronbach's alpha

Synopsis

CRONBACH(ref1,ref2,…)

Arguments

ref1: first data set

ref2: second data set

See also

VAR.

CVMTEST

CVMTEST Cramér-von Mises Test of Normality

Synopsis

CVMTEST(x)

Arguments

x: array of sample values

Description

This function returns an array with the first row giving the p-value of the Cramér-von Mises Test, the second row the test statistic of the test, and the third the number of observations in the sample.

Note

If there are less than 8 sample values, CVMTEST returns #VALUE!

See also

CHITEST, ADTEST, LKSTEST, SFTEST.

DEVSQ

DEVSQ sum of squares of deviations of a data set

Synopsis

DEVSQ(number1,number2,…)

Arguments

number1: first value

number2: second value

Description

Strings and empty cells are simply ignored.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

STDEV.

EXPONDIST

EXPONDIST probability density or cumulative distribution function of the exponential distribution

Synopsis

EXPONDIST(x,y,cumulative)

Arguments

x: number

y: scale parameter

cumulative: whether to evaluate the density function or the cumulative distribution function

Description

If cumulative is false it will return: y * exp (-y*x),otherwise it will return 1 - exp (-y*x).

Note

If x < 0 or y <= 0 this will return an error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

POISSON.

EXPPOWDIST

EXPPOWDIST the probability density function of the Exponential Power distribution

Synopsis

EXPPOWDIST(x,a,b)

Arguments

x: number

a: scale parameter

b: scale parameter

Description

This distribution has been recommended for lifetime analysis when a U-shaped hazard function is desired. This corresponds to rapid failure once the product starts to wear out after a period of steady or even improving reliability.

See also

RANDEXPPOW.

FDIST

FDIST survival function of the F distribution

Synopsis

FDIST(x,dof_of_num,dof_of_denom)

Arguments

x: number

dof_of_num: numerator degrees of freedom

dof_of_denom: denominator degrees of freedom

Description

The survival function is 1 minus the cumulative distribution function.

Note

If x < 0 this function returns a #NUM! error. If dof_of_num < 1 or dof_of_denom < 1, this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

OpenDocument Format (ODF) Compatibility

FDIST is the OpenFormula function LEGACY.FDIST.

See also

FINV.

FINV

FINV inverse of the survival function of the F distribution

Synopsis

FINV(p,dof_of_num,dof_of_denom)

Arguments

p: probability

dof_of_num: numerator degrees of freedom

dof_of_denom: denominator degrees of freedom

Description

The survival function is 1 minus the cumulative distribution function.

Note

If p < 0 or p > 1 this function returns a #NUM! error. If dof_of_num < 1 or dof_of_denom < 1 this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

OpenDocument Format (ODF) Compatibility

FINV is the OpenFormula function LEGACY.FINV.

See also

FDIST.

FISHER

FISHER Fisher transformation

Synopsis

FISHER(x)

Arguments

x: number

Note

If x is not a number, this function returns a #VALUE! error. If x <= -1 or x >= 1, this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

FISHERINV, ATANH.

FISHERINV

FISHERINV inverse of the Fisher transformation

Synopsis

FISHERINV(x)

Arguments

x: number

Note

If x is a non-number this function returns a #VALUE! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

FISHER, TANH.

FORECAST

FORECAST estimates a future value according to existing values using simple linear regression

Synopsis

FORECAST(x,known_ys,known_xs)

Arguments

x: x-value whose matching y-value should be forecast

known_ys: known y-values

known_xs: known x-values

Description

This function estimates a future value according to existing values using simple linear regression.

Note

If known_xs or known_ys contains no data entries or different number of data entries, this function returns a #N/A error. If the variance of the known_xs is zero, this function returns a #DIV/0 error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

INTERCEPT, TREND.

FREQUENCY

FREQUENCY frequency table

Synopsis

FREQUENCY(data_array,bins_array)

Arguments

data_array: data values

bins_array: array of cutoff values

Description

The results are given as an array.

If the bins_array is empty, this function returns the number of data points in data_array.

Microsoft Excel Compatibility

This function is Excel compatible.

FTEST

FTEST p-value for the two-tailed hypothesis test comparing the variances of two populations

Synopsis

FTEST(array1,array2)

Arguments

array1: sample from the first population

array2: sample from the second population

Microsoft Excel Compatibility

This function is Excel compatible.

See also

FDIST, FINV.

GAMMADIST

GAMMADIST probability density or cumulative distribution function of the gamma distribution

Synopsis

GAMMADIST(x,alpha,beta,cumulative)

Arguments

x: number

alpha: scale parameter

beta: scale parameter

cumulative: whether to evaluate the density function or the cumulative distribution function

Note

If x < 0 this function returns a #NUM! error. If alpha <= 0 or beta <= 0, this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

GAMMAINV.

GAMMAINV

GAMMAINV inverse of the cumulative gamma distribution

Synopsis

GAMMAINV(p,alpha,beta)

Arguments

p: probability

alpha: scale parameter

beta: scale parameter

Note

If p < 0 or p > 1 this function returns a #NUM! error. If alpha <= 0 or beta <= 0 this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

GAMMADIST.

GEOMDIST

GEOMDIST probability mass or cumulative distribution function of the geometric distribution

Synopsis

GEOMDIST(k,p,cumulative)

Arguments

k: number of trials

p: probability of success in any trial

cumulative: whether to evaluate the mass function or the cumulative distribution function

Note

If k < 0 this function returns a #NUM! error. If p < 0 or p > 1 this function returns a #NUM! error. If cumulative is neither TRUE nor FALSE this function returns a #VALUE! error.

See also

RANDGEOM.

GEOMEAN

GEOMEAN geometric mean

Synopsis

GEOMEAN(number1,number2,…)

Arguments

number1: first value

number2: second value

Description

The geometric mean is equal to the Nth root of the product of the N values.

Microsoft Excel Compatibility

This function is Excel compatible.

GROWTH

GROWTH exponential growth prediction

Synopsis

GROWTH(known_ys,known_xs,new_xs,affine)

Arguments

known_ys: known y-values

known_xs: known x-values; defaults to the array {1, 2, 3, …}

new_xs: x-values for which to estimate the y-values; defaults to known_xs

affine: if true, the model contains a constant term, defaults to true

Description

GROWTH function applies the “least squares” method to fit an exponential curve to your data and predicts the exponential growth by using this curve.

GROWTH returns an array having one column and a row for each data point in new_xs.

Note

If known_ys and known_xs have unequal number of data points, this function returns a #NUM! error.

See also

LOGEST, GROWTH, TREND.

HARMEAN

HARMEAN harmonic mean

Synopsis

HARMEAN(number1,number2,…)

Arguments

number1: first value

number2: second value

Description

The harmonic mean of N data points is N divided by the sum of the reciprocals of the data points).

Microsoft Excel Compatibility

This function is Excel compatible.

HYPGEOMDIST

HYPGEOMDIST probability mass or cumulative distribution function of the hypergeometric distribution

Synopsis

HYPGEOMDIST(x,n,M,N,cumulative)

Arguments

x: number of successes

n: sample size

M: number of possible successes in the population

N: population size

cumulative: whether to evaluate the mass function or the cumulative distribution function

Note

If x,n,M or N is a non-integer it is truncated. If x,n,M or N < 0 this function returns a #NUM! error. If x > M or n > N this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

BINOMDIST, POISSON.

INTERCEPT

INTERCEPT the intercept of a linear regression line

Synopsis

INTERCEPT(known_ys,known_xs)

Arguments

known_ys: known y-values

known_xs: known x-values

Note

If known_xs or known_ys contains no data entries or different number of data entries, this function returns a #N/A error. If the variance of the known_xs is zero, this function returns #DIV/0 error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

FORECAST, TREND.

KURT

KURT unbiased estimate of the kurtosis of a data set

Synopsis

KURT(number1,number2,…)

Arguments

number1: first value

number2: second value

Description

Strings and empty cells are simply ignored.

Note

This is only meaningful if the underlying distribution really has a fourth moment. The kurtosis is offset by three such that a normal distribution will have zero kurtosis. If fewer than four numbers are given or all of them are equal this function returns a #DIV/0! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

AVERAGE, VAR, SKEW, KURTP.

KURTP

KURTP population kurtosis of a data set

Synopsis

KURTP(number1,number2,…)

Arguments

number1: first value

number2: second value

Description

Strings and empty cells are simply ignored.

Note

If fewer than two numbers are given or all of them are equal this function returns a #DIV/0! error.

See also

AVERAGE, VARP, SKEWP, KURT.

LANDAU

LANDAU approximate probability density function of the Landau distribution

Synopsis

LANDAU(x)

Arguments

x: number

See also

RANDLANDAU.

LAPLACE

LAPLACE probability density function of the Laplace distribution

Synopsis

LAPLACE(x,a)

Arguments

x: number

a: mean

See also

RANDLAPLACE.

LARGE

LARGE k-th largest value in a data set

Synopsis

LARGE(data,k)

Arguments

data: data set

k: which value to find

Note

If data set is empty this function returns a #NUM! error. If k <= 0 or k is greater than the number of data items given this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

LEVERAGE

LEVERAGE calculate regression leverage

Synopsis

LEVERAGE(A)

Arguments

A: a matrix

Description

Returns the diagonal of A (A^T A)^-1 A^T as a column vector.

Note

If the matrix is singular, #VALUE! is returned.

LINEST

LINEST multiple linear regression coefficients and statistics

Synopsis

LINEST(known_ys,known_xs,affine,stats)

Arguments

known_ys: vector of values of dependent variable

known_xs: array of values of independent variables, defaults to a single vector {1,…,n}

affine: if true, the model contains a constant term, defaults to true

stats: if true, some additional statistics are provided, defaults to false

Description

This function returns an array with the first row giving the regression coefficients for the independent variables x_m, x_(m-1),…,x_2, x_1 followed by the y-intercept if affine is true.

If stats is true, the second row contains the corresponding standard errors of the regression coefficients.In this case, the third row contains the R^2 value and the standard error for the predicted value. The fourth row contains the observed F value and its degrees of freedom. Finally, the fifth row contains the regression sum of squares and the residual sum of squares.

If affine is false, R^2 is the uncentered version of the coefficient of determination; that is the proportion of the sum of squares explained by the model.

Note

If the length of known_ys does not match the corresponding length of known_xs, this function returns a #NUM! error.

See also

LOGEST, TREND.

LKSTEST

LKSTEST Lilliefors (Kolmogorov-Smirnov) Test of Normality

Synopsis

LKSTEST(x)

Arguments

x: array of sample values

Description

This function returns an array with the first row giving the p-value of the Lilliefors (Kolmogorov-Smirnov) Test, the second row the test statistic of the test, and the third the number of observations in the sample.

Note

If there are less than 5 sample values, LKSTEST returns #VALUE!

See also

CHITEST, ADTEST, SFTEST, CVMTEST.

LOGEST

LOGEST exponential least square fit

Synopsis

LOGEST(known_ys,known_xs,affine,stat)

Arguments

known_ys: known y-values

known_xs: known x-values; default to an array {1, 2, 3, …}

affine: if true, the model contains a constant term, defaults to true

stat: if true, extra statistical information will be returned; defaults to FALSE

Description

LOGEST function applies the “least squares” method to fit an exponential curve of the form y = b * m{1}^x{1} * m{2}^x{2}... to your data.

LOGEST returns an array { m{n},m{n-1}, ...,m{1},b }.

Note

Extra statistical information is written below the regression line coefficients in the result array. Extra statistical information consists of four rows of data. In the first row the standard error values for the coefficients m1, (m2, ...), b are represented. The second row contains the square of R and the standard error for the y estimate. The third row contains the F-observed value and the degrees of freedom. The last row contains the regression sum of squares and the residual sum of squares. If known_ys and known_xs have unequal number of data points, this function returns a #NUM! error.

See also

GROWTH, TREND.

LOGFIT

LOGFIT logarithmic least square fit (using a trial and error method)

Synopsis

LOGFIT(known_ys,known_xs)

Arguments

known_ys: known y-values

known_xs: known x-values

Description

LOGFIT function applies the “least squares” method to fit the logarithmic equation y = a + b * ln(sign * (x - c)) , sign = +1 or -1 to your data. The graph of the equation is a logarithmic curve moved horizontally by c and possibly mirrored across the y-axis (if sign = -1).

LOGFIT returns an array having five columns and one row. `Sign' is given in the first column, `a', `b', and `c' are given in columns 2 to 4. Column 5 holds the sum of squared residuals.

Note

An error is returned when there are less than 3 different x's or y's, or when the shape of the point cloud is too different from a ``logarithmic'' one. You can use the above formula = a + b * ln(sign * (x - c)) or rearrange it to = (exp((y - a) / b)) / sign + c to compute unknown y's or x's, respectively. This is non-linear fitting by trial-and-error. The accuracy of `c' is: width of x-range -> rounded to the next smaller (10^integer), times 0.000001. There might be cases in which the returned fit is not the best possible.

See also

LOGREG, LINEST, LOGEST.

LOGINV

LOGINV inverse of the cumulative distribution function of the lognormal distribution

Synopsis

LOGINV(p,mean,stddev)

Arguments

p: probability

mean: mean

stddev: standard deviation

Note

If p < 0 or p > 1 or stddev <= 0 this function returns #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

EXP, LN, LOG, LOG10, LOGNORMDIST.

LOGISTIC

LOGISTIC probability density function of the logistic distribution

Synopsis

LOGISTIC(x,a)

Arguments

x: number

a: scale parameter

See also

RANDLOGISTIC.

LOGNORMDIST

LOGNORMDIST cumulative distribution function of the lognormal distribution

Synopsis

LOGNORMDIST(x,mean,stddev)

Arguments

x: number

mean: mean

stddev: standard deviation

Note

If stddev = 0 LOGNORMDIST returns a #DIV/0! error. If x <= 0, mean < 0 or stddev <= 0 this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

NORMDIST.

LOGREG

LOGREG the logarithmic regression

Synopsis

LOGREG(known_ys,known_xs,affine,stat)

Arguments

known_ys: known y-values

known_xs: known x-values; defaults to the array {1, 2, 3, …}

affine: if true, the model contains a constant term, defaults to true

stat: if true, extra statistical information will be returned; defaults to FALSE

Description

LOGREG function transforms your x's to z=ln(x) and applies the “least squares” method to fit the linear equation y = m * z + b to your y's and z's --- equivalent to fitting the equation y = m * ln(x) + b to y's and x's. LOGREG returns an array having two columns and one row. m is given in the first column and b in the second.

Any extra statistical information is written below m and b in the result array. This extra statistical information consists of four rows of data: In the first row the standard error values for the coefficients m, b are given. The second row contains the square of R and the standard error for the y estimate. The third row contains the F-observed value and the degrees of freedom. The last row contains the regression sum of squares and the residual sum of squares.The default of stat is FALSE.

Note

If known_ys and known_xs have unequal number of data points, this function returns a #NUM! error.

See also

LOGFIT, LINEST, LOGEST.

MAX

MAX largest value, with negative numbers considered smaller than positive numbers

Synopsis

MAX(number1,number2,…)

Arguments

number1: first value

number2: second value

Microsoft Excel Compatibility

This function is Excel compatible.

See also

MIN, ABS.

MAXA

MAXA largest value, with negative numbers considered smaller than positive numbers

Synopsis

MAXA(number1,number2,…)

Arguments

number1: first value

number2: second value

Description

Numbers, text and logical values are included in the calculation too. If the cell contains text or the argument evaluates to FALSE, it is counted as value zero (0). If the argument evaluates to TRUE, it is counted as one (1). Note that empty cells are not counted.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

MAX, MINA.

MEDIAN

MEDIAN median of a data set

Synopsis

MEDIAN(number1,number2,…)

Arguments

number1: first value

number2: second value

Description

Strings and empty cells are simply ignored.

Note

If even numbers are given MEDIAN returns the average of the two numbers in the center.

Microsoft Excel Compatibility

This function is Excel compatible.

MIN

MIN smallest value, with negative numbers considered smaller than positive numbers

Synopsis

MIN(number1,number2,…)

Arguments

number1: first value

number2: second value

Microsoft Excel Compatibility

This function is Excel compatible.

See also

MAX, ABS.

MINA

MINA smallest value, with negative numbers considered smaller than positive numbers

Synopsis

MINA(number1,number2,…)

Arguments

number1: first value

number2: second value

Description

Numbers, text and logical values are included in the calculation too. If the cell contains text or the argument evaluates to FALSE, it is counted as value zero (0). If the argument evaluates to TRUE, it is counted as one (1). Note that empty cells are not counted.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

MIN, MAXA.

MODE

MODE first most common number in the dataset

Synopsis

MODE(number1,number2,…)

Arguments

number1: first value

number2: second value

Description

Strings and empty cells are simply ignored.

If the data set does not contain any duplicates this function returns a #N/A error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

AVERAGE, MEDIAN, MODE.MULT.

MODE.MULT

MODE.MULT most common numbers in the dataset

Synopsis

MODE.MULT(number1,number2,…)

Arguments

number1: first value

number2: second value

Description

Strings and empty cells are simply ignored.

If the data set does not contain any duplicates this function returns a #N/A error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

AVERAGE, MEDIAN, MODE.

NEGBINOMDIST

NEGBINOMDIST probability mass function of the negative binomial distribution

Synopsis

NEGBINOMDIST(f,t,p)

Arguments

f: number of failures

t: threshold number of successes

p: probability of a success

Note

If f or t is a non-integer it is truncated. If (f + t -1) <= 0 this function returns a #NUM! error. If p < 0 or p > 1 this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

NORMDIST

NORMDIST probability density or cumulative distribution function of a normal distribution

Synopsis

NORMDIST(x,mean,stddev,cumulative)

Arguments

x: number

mean: mean of the distribution

stddev: standard deviation of the distribution

cumulative: whether to evaluate the density function or the cumulative distribution function

Note

If stddev is 0 this function returns a #DIV/0! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

POISSON.

NORMINV

NORMINV inverse of the cumulative distribution function of a normal distribution

Synopsis

NORMINV(p,mean,stddev)

Arguments

p: probability

mean: mean of the distribution

stddev: standard deviation of the distribution

Note

If p < 0 or p > 1 or stddev <= 0 this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

NORMSDIST

NORMSDIST cumulative distribution function of the standard normal distribution

Synopsis

NORMSDIST(x)

Arguments

x: number

Microsoft Excel Compatibility

This function is Excel compatible.

OpenDocument Format (ODF) Compatibility

NORMSDIST is the OpenFormula function LEGACY.NORMSDIST.

See also

NORMDIST.

NORMSINV

NORMSINV inverse of the cumulative distribution function of the standard normal distribution

Synopsis

NORMSINV(p)

Arguments

p: given probability

Note

If p < 0 or p > 1 this function returns #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

OpenDocument Format (ODF) Compatibility

NORMSINV is the OpenFormula function LEGACY.NORMSINV.

OWENT

OWENT Owen's T function

Synopsis

OWENT(h,a)

Arguments

h: number

a: number

See also

R.PSNORM, R.PST.

PARETO

PARETO probability density function of the Pareto distribution

Synopsis

PARETO(x,a,b)

Arguments

x: number

a: exponent

b: scale parameter

See also

RANDPARETO.

PEARSON

PEARSON Pearson correlation coefficient of the paired set of data

Synopsis

PEARSON(array1,array2)

Arguments

array1: first component values

array2: second component values

Description

Strings and empty cells are simply ignored.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

INTERCEPT, LINEST, RSQ, SLOPE, STEYX.

PERCENTILE

PERCENTILE determines the 100*k-th percentile of the given data points (Hyndman-Fan method 7: N-1 basis)

Synopsis

PERCENTILE(array,k)

Arguments

array: data points

k: which percentile to calculate

Note

If array is empty, this function returns a #NUM! error. If k < 0 or k > 1, this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

QUARTILE.

PERCENTILE.EXC

PERCENTILE.EXC determines the 100*k-th percentile of the given data points (Hyndman-Fan method 6: N+1 basis)

Synopsis

PERCENTILE.EXC(array,k)

Arguments

array: data points

k: which percentile to calculate

Note

If array is empty, this function returns a #NUM! error. If k < 0 or k > 1, this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

PERCENTRANK

PERCENTRANK rank of a data point in a data set (Hyndman-Fan method 7: N-1 basis)

Synopsis

PERCENTRANK(array,x,significance)

Arguments

array: range of numeric values

x: data point to be ranked

significance: number of significant digits, defaults to 3

Note

If array contains no data points, this function returns a #NUM! error. If significance is less than one, this function returns a #NUM! error. If x exceeds the largest value or is less than the smallest value in array, this function returns an #N/A! error. If x does not match any of the values in array or x matches more than once, this function interpolates the returned value.

PERCENTRANK.EXC

PERCENTRANK.EXC rank of a data point in a data set (Hyndman-Fan method 6: N+1 basis)

Synopsis

PERCENTRANK.EXC(array,x,significance)

Arguments

array: range of numeric values

x: data point to be ranked

significance: number of significant digits, defaults to 3

Note

If array contains no data points, this function returns a #NUM! error. If significance is less than one, this function returns a #NUM! error. If x exceeds the largest value or is less than the smallest value in array, this function returns an #N/A! error. If x does not match any of the values in array or x matches more than once, this function interpolates the returned value.

PERMUT

PERMUT number of k-permutations of a n-set

Synopsis

PERMUT(n,k)

Arguments

n: size of the base set

k: number of elements in each permutation

Note

If n = 0 this function returns a #NUM! error. If n < k this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

COMBIN.

PERMUTATIONA

PERMUTATIONA the number of permutations of y objects chosen from x objects with repetition allowed

Synopsis

PERMUTATIONA(x,y)

Arguments

x: total number of objects

y: number of selected objects

Note

If both x and y equal 0, PERMUTATIONA returns 1. If x < 0 or y < 0, PERMUTATIONA returns #NUM! If x or y are not integers, they are truncated.

OpenDocument Format (ODF) Compatibility

This function is OpenFormula compatible.

See also

POWER.

POISSON

POISSON probability mass or cumulative distribution function of the Poisson distribution

Synopsis

POISSON(x,mean,cumulative)

Arguments

x: number of events

mean: mean of the distribution

cumulative: whether to evaluate the mass function or the cumulative distribution function

Note

If x is a non-integer it is truncated. If x < 0 this function returns a #NUM! error. If mean <= 0 POISSON returns the #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

NORMDIST, WEIBULL.

PROB

PROB probability of an interval for a discrete (and finite) probability distribution

Synopsis

PROB(x_range,prob_range,lower_limit,upper_limit)

Arguments

x_range: possible values

prob_range: probabilities of the corresponding values

lower_limit: lower interval limit

upper_limit: upper interval limit, defaults to lower_limit

Note

If the sum of the probabilities in prob_range is not equal to 1 this function returns a #NUM! error. If any value in prob_range is <=0 or > 1, this function returns a #NUM! error. If x_range and prob_range contain a different number of data entries, this function returns a #N/A error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

BINOMDIST, CRITBINOM.

QUARTILE

QUARTILE the k-th quartile of the data points (Hyndman-Fan method 7: N-1 basis)

Synopsis

QUARTILE(array,quart)

Arguments

array: data points

quart: a number from 0 to 4, indicating which quartile to calculate

Note

If array is empty, this function returns a #NUM! error. If quart < 0 or quart > 4, this function returns a #NUM! error. If quart = 0, the smallest value of array to be returned. If quart is not an integer, it is truncated.

Microsoft Excel Compatibility

This function is Excel compatible.

QUARTILE.EXC

QUARTILE.EXC the k-th quartile of the data points (Hyndman-Fan method 6: N+1 basis)

Synopsis

QUARTILE.EXC(array,quart)

Arguments

array: data points

quart: a number from 1 to 3, indicating which quartile to calculate

Note

If array is empty, this function returns a #NUM! error. If quart < 0 or quart > 4, this function returns a #NUM! error. If quart = 0, the smallest value of array to be returned. If quart is not an integer, it is truncated.

Microsoft Excel Compatibility

This function is Excel compatible.

R.DBETA

R.DBETA probability density function of the beta distribution

Synopsis

R.DBETA(x,a,b,give_log)

Arguments

x: observation

a: the first shape parameter of the distribution

b: the second scale parameter of the distribution

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the beta distribution.

See also

R.PBETA, R.QBETA.

R.DBINOM

R.DBINOM probability density function of the binomial distribution

Synopsis

R.DBINOM(x,n,psuc,give_log)

Arguments

x: observation

n: the number of trials

psuc: the probability of success in each trial

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the binomial distribution.

See also

R.PBINOM, R.QBINOM.

R.DCAUCHY

R.DCAUCHY probability density function of the Cauchy distribution

Synopsis

R.DCAUCHY(x,location,scale,give_log)

Arguments

x: observation

location: the center of the distribution

scale: the scale parameter of the distribution

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the Cauchy distribution.

See also

R.PCAUCHY, R.QCAUCHY.

R.DCHISQ

R.DCHISQ probability density function of the chi-square distribution

Synopsis

R.DCHISQ(x,df,give_log)

Arguments

x: observation

df: the number of degrees of freedom of the distribution

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the chi-square distribution.

OpenDocument Format (ODF) Compatibility

A two argument invocation R.DCHISQ(x,df) is exported to OpenFormula as CHISQDIST(x,df,FALSE()).

See also

R.PCHISQ, R.QCHISQ.

R.DEXP

R.DEXP probability density function of the exponential distribution

Synopsis

R.DEXP(x,scale,give_log)

Arguments

x: observation

scale: the scale parameter of the distribution

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the exponential distribution.

See also

R.PEXP, R.QEXP.

R.DF

R.DF probability density function of the F distribution

Synopsis

R.DF(x,n1,n2,give_log)

Arguments

x: observation

n1: the first number of degrees of freedom of the distribution

n2: the second number of degrees of freedom of the distribution

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the F distribution.

See also

R.PF, R.QF.

R.DGAMMA

R.DGAMMA probability density function of the gamma distribution

Synopsis

R.DGAMMA(x,shape,scale,give_log)

Arguments

x: observation

shape: the shape parameter of the distribution

scale: the scale parameter of the distribution

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the gamma distribution.

See also

R.PGAMMA, R.QGAMMA.

R.DGEOM

R.DGEOM probability density function of the geometric distribution

Synopsis

R.DGEOM(x,psuc,give_log)

Arguments

x: observation

psuc: the probability of success in each trial

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the geometric distribution.

See also

R.PGEOM, R.QGEOM.

R.DGUMBEL

R.DGUMBEL probability density function of the Gumbel distribution

Synopsis

R.DGUMBEL(x,mu,beta,give_log)

Arguments

x: observation

mu: the location parameter of freedom of the distribution

beta: the scale parameter of freedom of the distribution

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the Gumbel distribution.

See also

R.PGUMBEL, R.QGUMBEL.

R.DHYPER

R.DHYPER probability density function of the hypergeometric distribution

Synopsis

R.DHYPER(x,r,b,n,give_log)

Arguments

x: observation

r: the number of red balls

b: the number of black balls

n: the number of balls drawn

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the hypergeometric distribution.

See also

R.PHYPER, R.QHYPER.

R.DLNORM

R.DLNORM probability density function of the log-normal distribution

Synopsis

R.DLNORM(x,logmean,logsd,give_log)

Arguments

x: observation

logmean: mean of the underlying normal distribution

logsd: standard deviation of the underlying normal distribution

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the log-normal distribution.

See also

R.PLNORM, R.QLNORM.

R.DNBINOM

R.DNBINOM probability density function of the negative binomial distribution

Synopsis

R.DNBINOM(x,n,psuc,give_log)

Arguments

x: observation (number of failures)

n: required number of successes

psuc: the probability of success in each trial

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the negative binomial distribution.

See also

R.PNBINOM, R.QNBINOM.

R.DNORM

R.DNORM probability density function of the normal distribution

Synopsis

R.DNORM(x,mu,sigma,give_log)

Arguments

x: observation

mu: mean of the distribution

sigma: standard deviation of the distribution

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the normal distribution.

See also

R.PNORM, R.QNORM.

R.DPOIS

R.DPOIS probability density function of the Poisson distribution

Synopsis

R.DPOIS(x,lambda,give_log)

Arguments

x: observation

lambda: the mean of the distribution

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the Poisson distribution.

See also

R.PPOIS, R.QPOIS.

R.DRAYLEIGH

R.DRAYLEIGH probability density function of the Rayleigh distribution

Synopsis

R.DRAYLEIGH(x,scale,give_log)

Arguments

x: observation

scale: the scale parameter of the distribution

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the Rayleigh distribution.

R.DSNORM

R.DSNORM probability density function of the skew-normal distribution

Synopsis

R.DSNORM(x,shape,location,scale,give_log)

Arguments

x: observation

shape: the shape parameter of the distribution

location: the location parameter of the distribution

scale: the scale parameter of the distribution

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the skew-normal distribution.

See also

R.PSNORM, R.QSNORM.

R.DST

R.DST probability density function of the skew-t distribution

Synopsis

R.DST(x,n,shape,give_log)

Arguments

x: observation

n: the number of degrees of freedom of the distribution

shape: the shape parameter of the distribution

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the skew-t distribution.

See also

R.PST, R.QST.

R.DT

R.DT probability density function of the Student t distribution

Synopsis

R.DT(x,n,give_log)

Arguments

x: observation

n: the number of degrees of freedom of the distribution

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the Student t distribution.

See also

R.PT, R.QT.

R.DWEIBULL

R.DWEIBULL probability density function of the Weibull distribution

Synopsis

R.DWEIBULL(x,shape,scale,give_log)

Arguments

x: observation

shape: the shape parameter of the distribution

scale: the scale parameter of the distribution

give_log: if true, log of the result will be returned instead

Description

This function returns the probability density function of the Weibull distribution.

See also

R.PWEIBULL, R.QWEIBULL.

R.PBETA

R.PBETA cumulative distribution function of the beta distribution

Synopsis

R.PBETA(x,a,b,lower_tail,log_p)

Arguments

x: observation

a: the first shape parameter of the distribution

b: the second scale parameter of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the beta distribution.

See also

R.DBETA, R.QBETA.

R.PBINOM

R.PBINOM cumulative distribution function of the binomial distribution

Synopsis

R.PBINOM(x,n,psuc,lower_tail,log_p)

Arguments

x: observation

n: the number of trials

psuc: the probability of success in each trial

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the binomial distribution.

See also

R.DBINOM, R.QBINOM.

R.PCAUCHY

R.PCAUCHY cumulative distribution function of the Cauchy distribution

Synopsis

R.PCAUCHY(x,location,scale,lower_tail,log_p)

Arguments

x: observation

location: the center of the distribution

scale: the scale parameter of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the Cauchy distribution.

See also

R.DCAUCHY, R.QCAUCHY.

R.PCHISQ

R.PCHISQ cumulative distribution function of the chi-square distribution

Synopsis

R.PCHISQ(x,df,lower_tail,log_p)

Arguments

x: observation

df: the number of degrees of freedom of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the chi-square distribution.

OpenDocument Format (ODF) Compatibility

A two argument invocation R.PCHISQ(x,df) is exported to OpenFormula as CHISQDIST(x,df).

See also

R.DCHISQ, R.QCHISQ.

R.PEXP

R.PEXP cumulative distribution function of the exponential distribution

Synopsis

R.PEXP(x,scale,lower_tail,log_p)

Arguments

x: observation

scale: the scale parameter of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the exponential distribution.

See also

R.DEXP, R.QEXP.

R.PF

R.PF cumulative distribution function of the F distribution

Synopsis

R.PF(x,n1,n2,lower_tail,log_p)

Arguments

x: observation

n1: the first number of degrees of freedom of the distribution

n2: the second number of degrees of freedom of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the F distribution.

See also

R.DF, R.QF.

R.PGAMMA

R.PGAMMA cumulative distribution function of the gamma distribution

Synopsis

R.PGAMMA(x,shape,scale,lower_tail,log_p)

Arguments

x: observation

shape: the shape parameter of the distribution

scale: the scale parameter of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the gamma distribution.

See also

R.DGAMMA, R.QGAMMA.

R.PGEOM

R.PGEOM cumulative distribution function of the geometric distribution

Synopsis

R.PGEOM(x,psuc,lower_tail,log_p)

Arguments

x: observation

psuc: the probability of success in each trial

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the geometric distribution.

See also

R.DGEOM, R.QGEOM.

R.PGUMBEL

R.PGUMBEL cumulative distribution function of the Gumbel distribution

Synopsis

R.PGUMBEL(x,mu,beta,lower_tail,log_p)

Arguments

x: observation

mu: the location parameter of freedom of the distribution

beta: the scale parameter of freedom of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the Gumbel distribution.

See also

R.DGUMBEL, R.QGUMBEL.

R.PHYPER

R.PHYPER cumulative distribution function of the hypergeometric distribution

Synopsis

R.PHYPER(x,r,b,n,lower_tail,log_p)

Arguments

x: observation

r: the number of red balls

b: the number of black balls

n: the number of balls drawn

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the hypergeometric distribution.

See also

R.DHYPER, R.QHYPER.

R.PLNORM

R.PLNORM cumulative distribution function of the log-normal distribution

Synopsis

R.PLNORM(x,logmean,logsd,lower_tail,log_p)

Arguments

x: observation

logmean: mean of the underlying normal distribution

logsd: standard deviation of the underlying normal distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the log-normal distribution.

See also

R.DLNORM, R.QLNORM.

R.PNBINOM

R.PNBINOM cumulative distribution function of the negative binomial distribution

Synopsis

R.PNBINOM(x,n,psuc,lower_tail,log_p)

Arguments

x: observation (number of failures)

n: required number of successes

psuc: the probability of success in each trial

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the negative binomial distribution.

See also

R.DNBINOM, R.QNBINOM.

R.PNORM

R.PNORM cumulative distribution function of the normal distribution

Synopsis

R.PNORM(x,mu,sigma,lower_tail,log_p)

Arguments

x: observation

mu: mean of the distribution

sigma: standard deviation of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the normal distribution.

See also

R.DNORM, R.QNORM.

R.PPOIS

R.PPOIS cumulative distribution function of the Poisson distribution

Synopsis

R.PPOIS(x,lambda,lower_tail,log_p)

Arguments

x: observation

lambda: the mean of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the Poisson distribution.

See also

R.DPOIS, R.QPOIS.

R.PRAYLEIGH

R.PRAYLEIGH cumulative distribution function of the Rayleigh distribution

Synopsis

R.PRAYLEIGH(x,scale,lower_tail,log_p)

Arguments

x: observation

scale: the scale parameter of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the Rayleigh distribution.

R.PSNORM

R.PSNORM cumulative distribution function of the skew-normal distribution

Synopsis

R.PSNORM(x,shape,location,scale,lower_tail,log_p)

Arguments

x: observation

shape: the shape parameter of the distribution

location: the location parameter of the distribution

scale: the scale parameter of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the skew-normal distribution.

See also

R.DSNORM, R.QSNORM.

R.PST

R.PST cumulative distribution function of the skew-t distribution

Synopsis

R.PST(x,n,shape,lower_tail,log_p)

Arguments

x: observation

n: the number of degrees of freedom of the distribution

shape: the shape parameter of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the skew-t distribution.

See also

R.DST, R.QST.

R.PT

R.PT cumulative distribution function of the Student t distribution

Synopsis

R.PT(x,n,lower_tail,log_p)

Arguments

x: observation

n: the number of degrees of freedom of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the Student t distribution.

See also

R.DT, R.QT.

R.PTUKEY

R.PTUKEY cumulative distribution function of the Studentized range distribution

Synopsis

R.PTUKEY(x,nmeans,df,nranges,lower_tail,log_p)

Arguments

x: observation

nmeans: the number of means

df: the number of degrees of freedom of the distribution

nranges: the number of ranges; default is 1

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the Studentized range distribution.

See also

R.QTUKEY.

R.PWEIBULL

R.PWEIBULL cumulative distribution function of the Weibull distribution

Synopsis

R.PWEIBULL(x,shape,scale,lower_tail,log_p)

Arguments

x: observation

shape: the shape parameter of the distribution

scale: the scale parameter of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the cumulative distribution function of the Weibull distribution.

See also

R.DWEIBULL, R.QWEIBULL.

R.QBETA

R.QBETA probability quantile function of the beta distribution

Synopsis

R.QBETA(p,a,b,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

a: the first shape parameter of the distribution

b: the second scale parameter of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the beta distribution.

See also

R.DBETA, R.PBETA.

R.QBINOM

R.QBINOM probability quantile function of the binomial distribution

Synopsis

R.QBINOM(p,n,psuc,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

n: the number of trials

psuc: the probability of success in each trial

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the binomial distribution.

See also

R.DBINOM, R.PBINOM.

R.QCAUCHY

R.QCAUCHY probability quantile function of the Cauchy distribution

Synopsis

R.QCAUCHY(p,location,scale,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

location: the center of the distribution

scale: the scale parameter of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the Cauchy distribution.

See also

R.DCAUCHY, R.PCAUCHY.

R.QCHISQ

R.QCHISQ probability quantile function of the chi-square distribution

Synopsis

R.QCHISQ(p,df,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

df: the number of degrees of freedom of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the chi-square distribution.

OpenDocument Format (ODF) Compatibility

A two argument invocation R.QCHISQ(p,df) is exported to OpenFormula as CHISQINV(p,df).

See also

R.DCHISQ, R.PCHISQ.

R.QEXP

R.QEXP probability quantile function of the exponential distribution

Synopsis

R.QEXP(p,scale,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

scale: the scale parameter of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the exponential distribution.

See also

R.DEXP, R.PEXP.

R.QF

R.QF probability quantile function of the F distribution

Synopsis

R.QF(p,n1,n2,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

n1: the first number of degrees of freedom of the distribution

n2: the second number of degrees of freedom of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the F distribution.

See also

R.DF, R.PF.

R.QGAMMA

R.QGAMMA probability quantile function of the gamma distribution

Synopsis

R.QGAMMA(p,shape,scale,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

shape: the shape parameter of the distribution

scale: the scale parameter of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the gamma distribution.

See also

R.DGAMMA, R.PGAMMA.

R.QGEOM

R.QGEOM probability quantile function of the geometric distribution

Synopsis

R.QGEOM(p,psuc,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

psuc: the probability of success in each trial

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the geometric distribution.

See also

R.DGEOM, R.PGEOM.

R.QGUMBEL

R.QGUMBEL probability quantile function of the Gumbel distribution

Synopsis

R.QGUMBEL(p,mu,beta,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

mu: the location parameter of freedom of the distribution

beta: the scale parameter of freedom of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the Gumbel distribution.

See also

R.DGUMBEL, R.PGUMBEL.

R.QHYPER

R.QHYPER probability quantile function of the hypergeometric distribution

Synopsis

R.QHYPER(p,r,b,n,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

r: the number of red balls

b: the number of black balls

n: the number of balls drawn

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the hypergeometric distribution.

See also

R.DHYPER, R.PHYPER.

R.QLNORM

R.QLNORM probability quantile function of the log-normal distribution

Synopsis

R.QLNORM(p,logmean,logsd,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

logmean: mean of the underlying normal distribution

logsd: standard deviation of the underlying normal distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the log-normal distribution.

See also

R.DLNORM, R.PLNORM.

R.QNBINOM

R.QNBINOM probability quantile function of the negative binomial distribution

Synopsis

R.QNBINOM(p,n,psuc,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

n: required number of successes

psuc: the probability of success in each trial

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the negative binomial distribution.

See also

R.DNBINOM, R.PNBINOM.

R.QNORM

R.QNORM probability quantile function of the normal distribution

Synopsis

R.QNORM(p,mu,sigma,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

mu: mean of the distribution

sigma: standard deviation of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the normal distribution.

See also

R.DNORM, R.PNORM.

R.QPOIS

R.QPOIS probability quantile function of the Poisson distribution

Synopsis

R.QPOIS(p,lambda,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

lambda: the mean of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the Poisson distribution.

See also

R.DPOIS, R.PPOIS.

R.QRAYLEIGH

R.QRAYLEIGH probability quantile function of the Rayleigh distribution

Synopsis

R.QRAYLEIGH(p,scale,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

scale: the scale parameter of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the Rayleigh distribution.

R.QSNORM

R.QSNORM probability quantile function of the skew-normal distribution

Synopsis

R.QSNORM(p,shape,location,scale,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

shape: the shape parameter of the distribution

location: the location parameter of the distribution

scale: the scale parameter of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the skew-normal distribution.

See also

R.DSNORM, R.PSNORM.

R.QST

R.QST probability quantile function of the skew-t distribution

Synopsis

R.QST(p,n,shape,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

n: the number of degrees of freedom of the distribution

shape: the shape parameter of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the skew-t distribution.

See also

R.DST, R.PST.

R.QT

R.QT probability quantile function of the Student t distribution

Synopsis

R.QT(p,n,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

n: the number of degrees of freedom of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the Student t distribution.

See also

R.DT, R.PT.

R.QTUKEY

R.QTUKEY probability quantile function of the Studentized range distribution

Synopsis

R.QTUKEY(p,nmeans,df,nranges,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

nmeans: the number of means

df: the number of degrees of freedom of the distribution

nranges: the number of ranges; default is 1

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the Studentized range distribution.

See also

R.PTUKEY.

R.QWEIBULL

R.QWEIBULL probability quantile function of the Weibull distribution

Synopsis

R.QWEIBULL(p,shape,scale,lower_tail,log_p)

Arguments

p: probability or natural logarithm of the probability

shape: the shape parameter of the distribution

scale: the scale parameter of the distribution

lower_tail: if true (the default), the lower tail of the distribution is considered

log_p: if true, the natural logarithm of the probability is given or returned; defaults to false

Description

This function returns the probability quantile function, i.e., the inverse of the cumulative distribution function, of the Weibull distribution.

See also

R.DWEIBULL, R.PWEIBULL.

RANK

RANK rank of a number in a list of numbers

Synopsis

RANK(x,ref,order)

Arguments

x: number whose rank you want to find

ref: list of numbers

order: 0 (descending order) or non-zero (ascending order); defaults to 0

Note

In case of a tie, RANK returns the largest possible rank.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

PERCENTRANK, RANK.AVG.

RANK.AVG

RANK.AVG rank of a number in a list of numbers

Synopsis

RANK.AVG(x,ref,order)

Arguments

x: number whose rank you want to find

ref: list of numbers

order: 0 (descending order) or non-zero (ascending order); defaults to 0

Note

In case of a tie, RANK.AVG returns the average rank.

Microsoft Excel Compatibility

This function is Excel 2010 compatible.

See also

PERCENTRANK, RANK.

RAYLEIGH

RAYLEIGH probability density function of the Rayleigh distribution

Synopsis

RAYLEIGH(x,sigma)

Arguments

x: number

sigma: scale parameter

See also

RANDRAYLEIGH.

RAYLEIGHTAIL

RAYLEIGHTAIL probability density function of the Rayleigh tail distribution

Synopsis

RAYLEIGHTAIL(x,a,sigma)

Arguments

x: number

a: lower limit

sigma: scale parameter

See also

RANDRAYLEIGHTAIL.

RSQ

RSQ square of the Pearson correlation coefficient of the paired set of data

Synopsis

RSQ(array1,array2)

Arguments

array1: first component values

array2: second component values

Description

Strings and empty cells are simply ignored.

Microsoft Excel Compatibility

This function is Excel compatible.

SFTEST

SFTEST Shapiro-Francia Test of Normality

Synopsis

SFTEST(x)

Arguments

x: array of sample values

Description

This function returns an array with the first row giving the p-value of the Shapiro-Francia Test, the second row the test statistic of the test, and the third the number of observations in the sample.

Note

If there are less than 5 or more than 5000 sample values, SFTEST returns #VALUE!

See also

CHITEST, ADTEST, LKSTEST, CVMTEST.

SKEW

SKEW unbiased estimate for skewness of a distribution

Synopsis

SKEW(number1,number2,…)

Arguments

number1: first value

number2: second value

Description

Strings and empty cells are simply ignored.

Note

This is only meaningful if the underlying distribution really has a third moment. The skewness of a symmetric (e.g., normal) distribution is zero. If less than three numbers are given, this function returns a #DIV/0! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

AVERAGE, VAR, SKEWP, KURT.

SKEWP

SKEWP population skewness of a data set

Synopsis

SKEWP(number1,number2,…)

Arguments

number1: first value

number2: second value

Description

Strings and empty cells are simply ignored.

Note

If less than two numbers are given, SKEWP returns a #DIV/0! error.

See also

AVERAGE, VARP, SKEW, KURTP.

SLOPE

SLOPE the slope of a linear regression line

Synopsis

SLOPE(known_ys,known_xs)

Arguments

known_ys: known y-values

known_xs: known x-values

Note

If known_xs or known_ys contains no data entries or different number of data entries, this function returns a #N/A error. If the variance of the known_xs is zero, this function returns #DIV/0 error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

STDEV, STDEVPA.

SMALL

SMALL k-th smallest value in a data set

Synopsis

SMALL(data,k)

Arguments

data: data set

k: which value to find

Note

If data set is empty this function returns a #NUM! error. If k <= 0 or k is greater than the number of data items given this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

SNORM.DIST.RANGE

SNORM.DIST.RANGE probability of the standard normal distribution over an interval

Synopsis

SNORM.DIST.RANGE(x1,x2)

Arguments

x1: start of the interval

x2: end of the interval

Description

This function returns the cumulative probability over a range of the standard normal distribution; that is the integral over the probability density function from x1 to x2.

Note

If x1>x2, this function returns a negative value.

SSMEDIAN

SSMEDIAN median for grouped data

Synopsis

SSMEDIAN(array,interval)

Arguments

array: data set

interval: length of each grouping interval, defaults to 1

Description

The data are assumed to be grouped into intervals of width interval. Each data point in array is the midpoint of the interval containing the true value. The median is calculated by interpolation within the median interval (the interval containing the median value), assuming that the true values within that interval are distributed uniformly:

median = L + interval*(N/2 - CF)/F

where:

L = the lower limit of the median interval

N = the total number of data points

CF = the number of data points below the median interval

F = the number of data points in the median interval

Note

If array is empty, this function returns a #NUM! error. If interval <= 0, this function returns a #NUM! error. SSMEDIAN does not check whether the data points are at least interval apart.

See also

MEDIAN.

STANDARDIZE

STANDARDIZE z-score of a value

Synopsis

STANDARDIZE(x,mean,stddev)

Arguments

x: value

mean: mean of the original distribution

stddev: standard deviation of the original distribution

Note

If stddev is 0 this function returns a #DIV/0! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

AVERAGE.

STDEV

STDEV sample standard deviation of the given sample

Synopsis

STDEV(area1,area2,…)

Arguments

area1: first cell area

area2: second cell area

Description

STDEV is also known as the N-1-standard deviation.

To obtain the population standard deviation of a whole population use STDEVP.

Microsoft Excel Compatibility

This function is Excel compatible.

STDEVA

STDEVA sample standard deviation of the given sample

Synopsis

STDEVA(area1,area2,…)

Arguments

area1: first cell area

area2: second cell area

Description

STDEVA is also known as the N-1-standard deviation.

To obtain the population standard deviation of a whole population use STDEVPA.

Numbers, text and logical values are included in the calculation too. If the cell contains text or the argument evaluates to FALSE, it is counted as value zero (0). If the argument evaluates to TRUE, it is counted as one (1). Note that empty cells are not counted.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

STDEV, STDEVPA.

STDEVP

STDEVP population standard deviation of the given population

Synopsis

STDEVP(area1,area2,…)

Arguments

area1: first cell area

area2: second cell area

Description

This is also known as the N-standard deviation

Microsoft Excel Compatibility

This function is Excel compatible.

See also

STDEV, STDEVA, STDEVPA.

STDEVPA

STDEVPA population standard deviation of an entire population

Synopsis

STDEVPA(area1,area2,…)

Arguments

area1: first cell area

area2: second cell area

Description

This is also known as the N-standard deviation

Numbers, text and logical values are included in the calculation too. If the cell contains text or the argument evaluates to FALSE, it is counted as value zero (0). If the argument evaluates to TRUE, it is counted as one (1). Note that empty cells are not counted.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

STDEVA, STDEVP.

STEYX

STEYX standard error of the predicted y-value in the regression

Synopsis

STEYX(known_ys,known_xs)

Arguments

known_ys: known y-values

known_xs: known x-values

Note

If known_ys and known_xs are empty or have a different number of arguments then this function returns a #N/A error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

PEARSON, RSQ, SLOPE.

SUBTOTAL

SUBTOTAL the subtotal of the given list of arguments

Synopsis

SUBTOTAL(function_nbr,ref1,ref2,…)

Arguments

function_nbr: determines which function to use according to the following table:

1 AVERAGE

2 COUNT

3 COUNTA

4 MAX

5 MIN

6 PRODUCT

7 STDEV

8 STDEVP

9 SUM

10 VAR

11 VARP

ref1: first value

ref2: second value

Microsoft Excel Compatibility

This function is Excel compatible.

See also

COUNT, SUM.

TDIST

TDIST survival function of the Student t-distribution

Synopsis

TDIST(x,dof,tails)

Arguments

x: number

dof: number of degrees of freedom

tails: 1 or 2

Description

The survival function is 1 minus the cumulative distribution function.

This function is Excel compatible for non-negative x.

Note

If dof < 1 this function returns a #NUM! error. If tails is neither 1 or 2 this function returns a #NUM! error. The parameterization of this function is different from what is used for, e.g., NORMSDIST. This is a common source of mistakes, but necessary for compatibility.

See also

TINV, TTEST.

TINV

TINV two tailed inverse of the Student t-distribution

Synopsis

TINV(p,dof)

Arguments

p: probability in both tails

dof: number of degrees of freedom

Description

This function returns the non-negative value x such that the area under the Student t density with dof degrees of freedom to the right of x is p/2.

Note

If p < 0 or p > 1 or dof < 1 this function returns a #NUM! error. The parameterization of this function is different from what is used for, e.g., NORMSINV. This is a common source of mistakes, but necessary for compatibility.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

TDIST, TTEST.

TREND

TREND estimates future values of a given data set using a least squares approximation

Synopsis

TREND(known_ys,known_xs,new_xs,affine)

Arguments

known_ys: vector of values of dependent variable

known_xs: array of values of independent variables, defaults to a single vector {1,…,n}

new_xs: array of x-values for which to estimate the y-values; defaults to known_xs

affine: if true, the model contains a constant term, defaults to true

Note

If the length of known_ys does not match the corresponding length of known_xs, this function returns a #NUM! error.

See also

LINEST.

TRIMMEAN

TRIMMEAN mean of the interior of a data set

Synopsis

TRIMMEAN(ref,fraction)

Arguments

ref: list of numbers whose mean you want to calculate

fraction: fraction of the data set excluded from the mean

Description

If fraction=0.2 and the data set contains 40 numbers, 8 numbers are trimmed from the data set (40 x 0.2): the 4 largest and the 4 smallest. To avoid a bias, the number of points to be excluded is always rounded down to the nearest even number.

Microsoft Excel Compatibility

This function is Excel compatible.

TTEST

TTEST p-value for a hypothesis test comparing the means of two populations using the Student t-distribution

Synopsis

TTEST(array1,array2,tails,type)

Arguments

array1: sample from the first population

array2: sample from the second population

tails: number of tails to consider

type: Type of test to perform. 1 indicates a test for paired variables, 2 a test of unpaired variables with equal variances, and 3 a test of unpaired variables with unequal variances

Note

If the data sets contain a different number of data points and the test is paired (type one), TTEST returns the #N/A error. tails and type are truncated to integers. If tails is not one or two, this function returns a #NUM! error. If type is any other than one, two, or three, this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

FDIST, FINV.

VAR

VAR sample variance of the given sample

Synopsis

VAR(area1,area2,…)

Arguments

area1: first cell area

area2: second cell area

Description

VAR is also known as the N-1-variance.

Note

Since the N-1-variance includes Bessel's correction, whereas the N-variance calculated by VARPA or VARP does not, under reasonable conditions the N-1-variance is an unbiased estimator of the variance of the population from which the sample is drawn.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

VARP, STDEV, VARA.

VARA

VARA sample variance of the given sample

Synopsis

VARA(area1,area2,…)

Arguments

area1: first cell area

area2: second cell area

Description

VARA is also known as the N-1-variance.

To get the true variance of a complete population use VARPA.

Numbers, text and logical values are included in the calculation too. If the cell contains text or the argument evaluates to FALSE, it is counted as value zero (0). If the argument evaluates to TRUE, it is counted as one (1). Note that empty cells are not counted.

Note

Since the N-1-variance includes Bessel's correction, whereas the N-variance calculated by VARPA or VARP does not, under reasonable conditions the N-1-variance is an unbiased estimator of the variance of the population from which the sample is drawn.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

VAR, VARPA.

VARP

VARP variance of an entire population

Synopsis

VARP(area1,area2,…)

Arguments

area1: first cell area

area2: second cell area

Description

VARP is also known as the N-variance.

See also

AVERAGE, DVAR, DVARP, STDEV, VAR.

VARPA

VARPA variance of an entire population

Synopsis

VARPA(area1,area2,…)

Arguments

area1: first cell area

area2: second cell area

Description

VARPA is also known as the N-variance.

Numbers, text and logical values are included in the calculation too. If the cell contains text or the argument evaluates to FALSE, it is counted as value zero (0). If the argument evaluates to TRUE, it is counted as one (1). Note that empty cells are not counted.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

VARA, VARP.

WEIBULL

WEIBULL probability density or cumulative distribution function of the Weibull distribution

Synopsis

WEIBULL(x,alpha,beta,cumulative)

Arguments

x: number

alpha: scale parameter

beta: scale parameter

cumulative: whether to evaluate the density function or the cumulative distribution function

Description

If the cumulative boolean is true it will return: 1 - exp (-(x/beta)^alpha),otherwise it will return (alpha/beta^alpha) * x^(alpha-1) * exp(-(x/beta^alpha)).

Note

If x < 0 this function returns a #NUM! error. If alpha <= 0 or beta <= 0 this function returns a #NUM! error.

Microsoft Excel Compatibility

This function is Excel compatible.

See also

POISSON.

ZTEST

ZTEST the probability of observing a sample mean as large as or larger than the mean of the given sample

Synopsis

ZTEST(ref,x,stddev)

Arguments

ref: data set (sample)

x: population mean

stddev: population standard deviation, defaults to the sample standard deviation

Description

ZTEST calculates the probability of observing a sample mean as large as or larger than the mean of the given sample for samples drawn from a normal distribution with mean x and standard deviation stddev.

Note

If ref contains less than two data items ZTEST returns #DIV/0! error.

Microsoft Excel Compatibility

This function is Excel compatible.

OpenDocument Format (ODF) Compatibility

This function is OpenFormula compatible.