Αποτελέσματα Αναζήτησης
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) inconsistencies...
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 techniques...
14 Ιαν 2013 · In summary, the decisions that researchers make about how to define, identify, and handle outliers have important implications. Specifically, such decisions change substantive conclusions including the presence or absence, direction, and size of an effect or relationship.
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...
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 ...
14 Ιαν 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) inconsistencies in how outliers are defined, identified, and handled in various methodological sources; and (c) confusion and lack of transparency in how outliers are ...
Why Are Outlier Analyses Important for Data Validity and Reliability? Outlier analysis is primarily important because it helps to identify errors in the data and, when investigated, may reveal systematic errors in data collection, coding, or entry.
