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Related Concept Videos

Unusual Results01:16

Unusual Results

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Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value =...
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What Are Outliers?01:12

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Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
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Outliers and Influential Points01:08

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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Detection of Gross Error: The Q Test01:00

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Data: Types and Distribution01:19

Data: Types and Distribution

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In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Data Discovery and Anomaly Detection Using Atypicality for Real-Valued Data.

Elyas Sabeti1, Anders Høst-Madsen2,3

  • 1Department of Computational Medicine and Bioinformatics, University of Michigan, NCRC 10-A108, 2800 Plymouth Rd, Ann Arbor, MI 48109-2800, USA.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

This study introduces a universal method to find unusual data by analyzing code length, extending previous work on discrete data to real-valued data using minimum description length (MDL). The approach was successfully applied to hydrophone and heart rate variability (HRV) signals.

Keywords:
atypicalitybig datacodelengthminimum description length

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Area of Science:

  • Data Science
  • Information Theory
  • Signal Processing

Background:

  • Extracting rare and interesting patterns from large datasets is challenging.
  • Existing statistical methods often focus on typical data, missing unusual insights.
  • Universal approaches are needed to identify unknown 'interesting' data features.

Purpose of the Study:

  • To extend the atypicality criterion based on code length to real-valued data.
  • To develop an information-theoretic methodology for universal signal processing models.
  • To apply the new methodology to real-world hydrophone and heart rate variability (HRV) signals.

Main Methods:

  • Utilizing the Minimum Description Length (MDL) principle for real-valued data analysis.
  • Developing universal, information-theoretic models for atypicality detection.
  • Applying the methodology to recorded hydrophone and HRV signals.

Main Results:

  • Successfully extended the code length-based atypicality method to real-valued data.
  • Demonstrated the applicability of MDL for universal signal processing models.
  • Identified interesting patterns in hydrophone and HRV datasets.

Conclusions:

  • The MDL-based approach provides a universal framework for detecting atypicality in real-valued data.
  • This method enhances data analysis by complementing typical statistics with rare event insights.
  • The findings have implications for analyzing complex signals in various scientific domains.