Jarque–Bera test

In statistics, the Jarque–Bera test is a goodness-of-fit test of whether sample data have the skewness and kurtosis matching a normal distribution. The test is named after Carlos Jarque and Anil K. Bera.The test statistic is always nonnegative. If it is far from zero, it signals the data do not have a normal distribution. The test statistic JB is defined as where n is the number of observations (or degrees of freedom in general); S is the sample skewness, K is the sample kurtosis : (These values have been approximated using Monte Carlo simulation in Matlab)

Jarque–Bera test

In statistics, the Jarque–Bera test is a goodness-of-fit test of whether sample data have the skewness and kurtosis matching a normal distribution. The test is named after Carlos Jarque and Anil K. Bera.The test statistic is always nonnegative. If it is far from zero, it signals the data do not have a normal distribution. The test statistic JB is defined as where n is the number of observations (or degrees of freedom in general); S is the sample skewness, K is the sample kurtosis : (These values have been approximated using Monte Carlo simulation in Matlab)