Problem of the Week # 159 - April 14, 2015

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SUMMARY

The interquartile range (IQR) of the standard normal distribution is approximately 1.349. Applying the 1.5×IQR rule, any z-scores below -2.698 or above 2.698 are classified as outliers. This analysis is crucial for understanding data distribution and identifying anomalies in statistical datasets. The correct solution was provided by user jacobi, demonstrating a clear understanding of the concepts involved.

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Here is this week's POTW:

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What is the $\text{IQR}$ of the standard normal distribution? Using the $1.5\times\text{IQR}$ rule, what $z$ scores would be considered outliers?

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Congratulations to jacobi for his correct solution. Here it is below:

The IQR of the standard normal distribution is $Q_3-Q_1$, where $Q_n$ is the nth quartile. To find the first and third quartiles, we have to find the points where the CDF of the normal distribution is $1 \over 4$ and $3 \over 4$, respectively, since $25\%$ and $75\%$ of the data are below the first and third quartiles, respectively.
The CDF of the normal distribution is \[ \frac{1}{\sigma \sqrt{2 \pi} }\int_{- \infty}^{x} e^{- \frac{1}{2} \left ( \frac{x-\mu}{\sigma} \right )^2} dx = \frac{1}{2} \left ( 1+ \operatorname{erf} \left ( \frac{x-\mu}{\sigma \sqrt{2}} \right ) \right ). \]
Therefore, the inverse of this is \[\mu + \sigma \sqrt{2} \operatorname{erf}^{-1} ( 2p-1), \] where p is the probability required. Evaluating this from $p=\frac{1}{4}$ to $p=\frac{3}{4}$, we get \[ 2 \sigma \sqrt{2} \operatorname{erf}^{-1} \left ( \frac{1}{2} \right ) \] as the IQR.
Therefore, the z scores considered outliers satisfy the condition \[ |z-\mu| > 3 \sigma \sqrt{2} \operatorname{erf}^{-1} \left ( \frac{1}{2} \right ). \]

[EDITOR'S NOTE] The standard deviation for the standard normal distribution is 1, and the mean is 0. Hence, the outliers will be \[ |z| > 3 \sqrt{2} \operatorname{erf}^{-1} \left ( \frac{1}{2} \right ). \]
 

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