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This guide provides an overview of outlier analysis. It is organized around four questions: 1. What is an outlier? 2. Why is outlier analysis important for data validity and reliability? 3. What action should states take after conducting an outlier analysis? 4. How can states conduct and display an outlier analysis? IDC’s principles for high ...
1 Ιαν 2005 · Outlier detection is a primary step in many data-mining applications. We present several methods for outlier detection, while distinguishing between univariate vs. multivariate...
30 Ιουν 2010 · Outliers are observations or measures that are suspicious because they are much smaller or much larger than the vast majority of the observations. These observations are problematic...
1 Απρ 2013 · Our literature review uncovered (a) 14 unique and mutually exclusive outlier definitions, 39 outlier identification techniques, and 20 different ways of handling outliers; (b)...
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...
This booklet concentrates on the practical aspects of dealing with outliers in data that arise most often in applications: single and multiple samples, linear regression, and factorial experiments.
We shall define an outlier in a set of data to be an observation (or subset of observations) which appears to be inconsistent with the remainder of that set of data. It is important to distinguish between outliers, which represent unexpectedly large deviations in a dataset, and noise, which represents low-level irregularity in a dataset.
