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## Volume 16 How To Detect And Handle Outliers 22.pdf

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Volume 16 How To Detect And Handle Outliers 22.pdf Volume 16 How To Detect And Handle Outliers 22.pdf Outlier detection is the process of recognizing observations that are too much of a deviation from the norm. An outlier is defined as an observation that falls outside a given statistical confidence interval. [1] Find outlier detections based on a single data point, e.g., [23] (if the outlier score is high) or by using a group of data points, e.g., [24] (if the outlier score is high) Volume 16 How To Detect And Handle Outliers 22.pdf Outlier detection is the process of recognizing observations that are too much of a deviation from the norm. An outlier is defined as an observation that falls outside a given statistical confidence interval. [1] Find outlier detections based on a single data point, e.g., [23] (if the outlier score is high) or by using a group of data points, e.g., [24] (if the outlier score is high) Volume 16 How To Detect And Handle Outliers 22.pdf 1Introduction Outlier detection is the process of recognizing observations that are too much of a deviation from the norm. An outlier is defined as an observation that falls outside a given statistical confidence interval. [1] Find outlier detections based on a single data point, e.g., [23] (if the outlier score is high) or by using a group of data points, e.g., [24] (if the outlier score is high) 1.1Outlier detection techniques Outlier detection is the process of recognizing observations that are too much of a deviation from the norm. An outlier is defined as an observation that falls outside a given statistical confidence interval. [1] Find outlier detections based on a single data point, e.g., [23] (if the outlier score is high) or by using a group of data points, e.g., [24] (if the outlier score is high) 1.1.1Outlier removal using a single data point In many applications, outliers can be detected by comparing the measured data points with a predicted value and removing the point that deviates most. Figure1.1 Outlier detection using a single data point However

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