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Lernen Challenge: Find Outliers in Sales Data | Data Quality Essentials
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bookChallenge: Find Outliers in Sales Data

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You will use the 1.5 * IQR rule to detect outliers in a vector of sales figures. Outliers can sometimes signal data entry errors or rare but valid events.

  • Calculate the first quartile (q1) and third quartile (q3) of the sales vector.
  • Compute the interquartile range (iqr) as q3 - q1.
  • Determine the lower and upper bounds for outliers using the 1.5 * IQR rule.
  • Identify the indices and values of any sales figures below the lower bound or above the upper bound.
  • Return a list with two elements: indices (the positions of outliers in the original vector) and values (the outlier sales figures).

Lösung

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Abschnitt 3. Kapitel 4
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bookChallenge: Find Outliers in Sales Data

Swipe um das Menü anzuzeigen

Aufgabe

Swipe to start coding

You will use the 1.5 * IQR rule to detect outliers in a vector of sales figures. Outliers can sometimes signal data entry errors or rare but valid events.

  • Calculate the first quartile (q1) and third quartile (q3) of the sales vector.
  • Compute the interquartile range (iqr) as q3 - q1.
  • Determine the lower and upper bounds for outliers using the 1.5 * IQR rule.
  • Identify the indices and values of any sales figures below the lower bound or above the upper bound.
  • Return a list with two elements: indices (the positions of outliers in the original vector) and values (the outlier sales figures).

Lösung

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War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 3. Kapitel 4
single

single

some-alt