Function vector_kurtosis (o2scl)

O2scl : Function List

template<class vec_t>
double o2scl::vector_kurtosis(size_t n, const vec_t &data, double mean, double stddev)

Kurtosis with specified mean and standard deviation.

This function computes

\[ -3 + \frac{1}{N} \sum_i \left[ \frac{ \left(x_i - \mu \right)}{ \sigma }\right]^4 \]

where the values of \( \mu \) and \( \sigma \) are given in mean and stddev.

This function produces the same results as gsl_stats_kurtosis_m_sd().

If n is zero, this function will return zero without calling the error handler.

template<class vec_t>
double o2scl::vector_kurtosis(const vec_t &data, double mean, double stddev)

Kurtosis with specified mean and standard deviation.

This function computes

\[ -3 + \frac{1}{N} \sum_i \left[ \frac{ \left(x_i - \mu \right)}{ \sigma }\right]^4 \]

where the values of \( \mu \) and \( \sigma \) are given in mean and stddev.

This function produces the same results as gsl_stats_kurtosis_m_sd().

If n is zero, this function will return zero without calling the error handler.

template<class vec_t>
double o2scl::vector_kurtosis(size_t n, const vec_t &data)

Kurtosis with computed mean and standard deviation.

This function computes

\[ -3 + \frac{1}{N} \sum_i \left[ \frac{ \left(x_i - \mu \right)}{ \sigma }\right]^4 \]

where the values of \( \mu \) and \( \sigma \) are computed using vector_mean() and vector_stddev().

This function produces the same results as gsl_stats_kurtosis().

If n is zero, this function will return zero without calling the error handler.

template<class vec_t>
double o2scl::vector_kurtosis(const vec_t &data)

Kurtosis with computed mean and standard deviation.

This function computes

\[ -3 + \frac{1}{N} \sum_i \left[ \frac{ \left(x_i - \mu \right)}{ \sigma }\right]^4 \]

where the values of \( \mu \) and \( \sigma \) are computed using vector_mean() and vector_stddev().

This function produces the same results as gsl_stats_kurtosis().

If n is zero, this function will return zero without calling the error handler.