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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...
14 Ιαν 2013 · For example, Table 1 includes definitions of outliers in the context of additional data-analytic approaches such as meta-analysis (e.g., influential meta-analysis effect size outlier), time series analysis (e.g., influential time series innovation outlier), and cluster analysis (e.g., cluster analysis outlier).
30 Ιουν 2010 · In case of GR log, the outliers are the measured values that are much smaller or much larger than the vast majority of the observations. ... Well Log Data Statistical Processing for Unbiased ...
24 Οκτ 2020 · This paper defines outliers to be focused on each production step and introduces practical methods to cope with them. The statistical production process is roughly divided into the following...
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.
Outliers are the numbers outside of a range of data considered normal. The following tutorials present different ways of identifying what is normal: . Tutorial 1: Systematically Determining What Is Normal Using the Interquartile Range. Tutorial 2: Qualitatively Defining a Normal Range. Tutorial 3: Simply Sorting.
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. Sometimes, for some reason or another, they should not be included in the analysis of the data.
