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Understanding Outliers and Clusters in Data Analysis

This guide explores the concepts of outliers and clusters in data sets. An outlier is a value that significantly differs from other data points, often considered as an "outsider." We illustrate this with examples like M&M counts and students' heights. Clusters are groups of data points that are close to each other in value. By analyzing these patterns, we can gain insights into data distribution and identify anomalies that need further investigation.

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Understanding Outliers and Clusters in Data Analysis

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  1. OutliersSeptember 27, 2012

  2. Outlier • An outlier is a number in a data set that is very different from the rest of the numbers. • “outsider” I Do Totals of M&Ms: 19, 16, 17, 17, 19, 9

  3. Cluster A cluster is several data points lie in a small interval

  4. I DO:Clusters and Outlier 18,20,21,24, 31,25,26,23,37

  5. We Do • Data: 5, 6, 23, 6, 8, 9, 8 Outlier _____ • Data: 78, 80, 82, 79, 105, 77, 79, 75, 76, 74, 76 Cluster Outlier _____

  6. You Do Heights of Students (Inches) • 65, 58, 60, 54,48, 63, 62, 59, 53 Cluster Outlier 2.1, 3.2, 3.6, 3.5, 3.9, 4.1, 3.9, 3.7, 3.9

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