Kurtosis measures the "tailedness" or peakedness of a probability distribution relative to a normal distribution. The formula for kurtosis is: \[ K = \frac{\mu_4}{\sigma^4} \] where \( \mu_4 \) is the fourth central moment and \( \sigma^2 \) is the variance. Given the values: Variance (\( \sigma^2 \)) = 3 Fourth central moment (\( \mu_4 \)) = 63 First, calculate \( \sigma^4 \): \[ \sigma^4 = (\sigma^2)^2 = 3^2 = 9 \] Now, compute the Kurtosis: \[ K = \frac{63}{9} = 7 \] A normal (Mesokurtic) distribution has a kurtosis of exactly 3. Since \( K = 7 > 3 \), the distribution has heavier tails and a higher peak compared to a normal distribution. Such a distribution is classified as Leptokurtic.