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f outliers in a set of data. We examine the different interests (from rejection of unacceptable contamination, through the accommodation of outliers with reduced influence in robust procedures applied to the whole set of data, to specific identification of outliers as the facets of pri.
In some data sets, there are values (observed data points) called outliers. Outliers are observed data points that are far from the least squares line. They have large "errors", where the "error" or residual is the vertical distance from the line to the point. Outliers need to be examined closely.
1 Ιαν 2004 · The goal of this paper is to summarize the various potential causes of extreme scores in a data set (e.g., data recording or entry errors, motivated mis-reporting, sampling errors, and...
1 Απρ 2013 · The presence of outliers, which are data points that deviate markedly from others, is one of the most enduring and pervasive methodological challenges in organizational science research. We...
14 Ιαν 2013 · The presence of outliers, which are data points that deviate markedly from others, is one of the most enduring and pervasive methodological challenges in organizational science research. We provide evidence that different ways of defining, identifying, and handling outliers alter substantive research conclusions.
30 Νοε 2021 · It’s important to carefully identify potential outliers in your dataset and deal with them in an appropriate manner for accurate results. There are four ways to identify outliers: Sorting method. Data visualization method. Statistical tests (z scores) Interquartile range method.
This work demonstrates how to identify outliers in electoral data using informatics methods and suggests that if the data contains significant outliers, the analyst should consider the use of robust statistical techniques.
