Calculate the Pearson's index of skewness. (Round the mean, median and standard deviation to one decimal place and Pearson's index to two decimal places.) \[ \mathrm{PI}=0.27 \] It can be concluded that the distribution is not significantly \( \mathbf{T} \) skewed.

Pearson's index of skewness is a handy statistic for understanding the symmetry of a dataset. It’s calculated by using the formula \( \mathrm{PI} = \frac{3(\text{Mean} - \text{Median})}{\text{Standard Deviation}} \). When the computed value is close to zero, it indicates that the data is approximately symmetric. In this case, with a skewness index of \( 0.27 \), it confirms that the distribution leans slightly toward the right but does not suggest significant skewness. Real-world applications of skewness can be found in finance, where understanding the distribution of asset returns is critical. A distribution that is skewed to the right might indicate the potential for higher returns, but with increased risk. Investors often use skewness to gauge risk and shape their investment strategies, aiming for a balance that meets their financial goals while considering the underlying risks associated with asymmetric returns.