VoseNBoot | Vose Software

# VoseNBoot

Example model

Use the set of VoseNBoot functions to estimate one or more population parameters via the non-parametric Bootstrap method.

##### VoseNBootCofV

VoseNBootCofV({data})

This function generates values for the non-parametric Bootstrap distribution of uncertainty for the coefficient of variance of a population given that one has observed random values from the population.

Data is an array of random values drawn from the population distribution. It must include at least two different values.

##### VoseNBootKurtosis

VoseNBootKurtosis({data})

This function generates values for the non-parametric Bootstrap distribution of uncertainty for the kurtosis of a population given that one has observed random values from the population.

Data is an array of random values drawn from the population distribution. It must include at least two different values.

##### VoseNBootMean

VoseNBootMean({data})

This function generates values for the non-parametric Bootstrap distribution of uncertainty for the mean of a population given that one has observed random values from the population.

Data is an array of random values drawn from the population distribution. It must include at least two different values.

##### VoseNBootPaired

VoseNBootPaired({data},direction)

Like it is done in the second stage of the non-parametric Bootstrap method, this function generates random samples from the data, which is an array of random values drawn from the population distribution.

Direction is a TRUE/FALSE switch that when is switched of or omitted, makes the function generate rows of samples from the dataset, an when switched columns of samples are generated.

##### VoseNBootPercentile
VoseNBootPercentile({data},percentile)

This function generates values for the non-parametric Bootstrap distribution of uncertainty for a certain specified percentile value of a population given that one has observed random values from the population.

Data is an array of random values drawn from the population distribution. It must include at least two different values.

##### VoseNBootSeries

VoseNBootSeries({datarange},datainrow)

This function takes a random sub-set of a series of data with length determined by the array size.

Datarange is the array of data and datainrow is a TRUE/FALSE switch to determine if the data should be looked at in rows or in columns.

##### VoseNBootSkewness

VoseNBootSkewness({data})

This function generates values for the non-parametric Bootstrap distribution of uncertainty for the skewness of a population given that one has observed random values from the population.

Data is an array of random values drawn from the population distribution. It must include at least two different values.

##### VoseNBootMoments

VoseNBootMoments({data})

This function generates values for the non-parametric Bootstrap distribution of uncertainty for the mean, variance, skewness and kurtosis of a population given that one has observed random values from the population.

Data is an array of random values drawn from the population distribution. It must include at least two different values.

This function should be used in cases where one is interested in more than one statistic at the same time.

### VoseNBootStDev

VoseNBootStDev({data})

This function generates values for the non-parametric Bootstrap distribution of uncertainty for the standard deviation of a population given that one has observed random values from the population.

Data is an array of random values drawn from the population distribution. It must include at least two different values.

##### VoseNBootVar

VoseNBootVar({data})

This function generates values for the non-parametric Bootstrap distribution of uncertainty for the variance of a population given that one has observed random values from the population.

Data is an array of random values drawn from the population distribution. It must include at least two different values.