Contents
What is multivariate kurtosis?
The mean multivariate skewness is 32.94, and the sample size-weighted mean is 28.26. The mean multivariate kurtosis is 78.70, and the sample size-weighted mean is 92.03. Therefore, the average skewness and kurtosis are greater than that of a multivariate normal distribution.
How do you interpret the coefficient of skewness and kurtosis?
A general guideline for skewness is that if the number is greater than +1 or lower than –1, this is an indication of a substantially skewed distribution. For kurtosis, the general guideline is that if the number is greater than +1, the distribution is too peaked.
When to use Mardia’s skewness test in Excel?
Mardia’s Skewness Test: If the sample comes from a multivariate normal distribution (null hypothesis), then For small samples (generally fewer than 20 sample elements), we have the following corrected statistic and df is as above. Mardia’s Kurtosis Test: If the sample comes from a multivariate normal distribution (null hypothesis), then
Which is the kurtosis test for multivariate normality?
Mardia’s Kurtosis Test: If the sample comes from a multivariate normal distribution (null hypothesis), then Real Statistics Functions: The Real Statistics Resource Pack provides the following array functions.
Which is the skewness test for multivariate normality?
MSKEWTEST(R1, lab): Mardia’s skewness test for multivariate normality; returns a column range with the values skewness, chi-square statistic, df and p-value, plus corrected statistic and p-value for small samples
What is the p value for the kurtosis test?
We see that p-value = .610 (or p-value = .535 using the correction factor for small samples) for the skewness test and p-value = .468 for the kurtosis test. Since all these p-values are larger than alpha = .05, we retain the null hypothesis and consider the sample as coming from a normal distribution.