Part 3 of 5 Calculate the Pearson's index of skewness. (Round the mean, median and standard deviation to one decimal place and Pearson's index to two decir places.) \[ \mathrm{PI}=\square \] It can be concluded that the distribution is not significantly \( \mathbf{\nabla} \) skewed. \( \square \) Try one last time Skip Part Recheck Save For Later © 2025 McGraw Hill LLC. All Riahts Resenved. Terms of Use I
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The Deep Dive
To calculate Pearson's index of skewness (PI), you'll need the mean, median, and standard deviation of your data set. The formula for Pearson's skewness is: \[ \text{PI} = \frac{3(\text{Mean} - \text{Median})}{\text{Standard Deviation}} \] After plugging in your values and rounding as indicated, you'll get your final skewness score. If the value of PI is close to 0 (typically between -0.5 and +0.5), you can conclude that the distribution is not significantly skewed. When analyzing data, keep in mind that a skewed distribution can affect statistical analyses like the t-test or ANOVA. After calculating the skewness, ensure it's properly interpreted within the context of your data, as it could suggest underlying trends or anomalies you might want to investigate further!