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Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
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Empirical Method to Interpret Standard Deviation01:09

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The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
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Coefficient of Variation01:10

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The coefficient of variation measures the dispersion of the data points or distribution around the mean. Using the coefficient of variation, we can compare two data series with drastically different means or different units of measurement. The coefficient of variation for a sample and a population is expressed as a percentage of the ratio of standard deviation to the mean.
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What is Variation?01:14

What is Variation?

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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
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Two-Way ANOVA01:17

Two-Way ANOVA

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The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
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Variation01:19

Variation

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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sJIVE: Supervised Joint and Individual Variation Explained.

Elise F Palzer1, Christine H Wendt2, Russell P Bowler3

  • 1Division of Biostatistics, University of Minnesota, Minneapolis, 55455, USA.

Computational Statistics & Data Analysis
|September 19, 2022
PubMed
Summary
This summary is machine-generated.

Supervised joint and individual variation explained (sJIVE) analyzes multi-source biomedical data to find shared and unique structures. This method improves prediction models, especially with noisy data, and was applied to COPDGene study data.

Keywords:
Data integrationDimension reductionGenomic dataHigh-dimensional predictionMulti-source dataMulti-view learning

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Area of Science:

  • Molecular biomedical research
  • Bioinformatics
  • Data analysis

Background:

  • Multi-source data analysis is common in molecular biomedical research.
  • Existing methods struggle to simultaneously identify data structures and build predictive models.
  • Current approaches either focus only on shared structures or ignore outcome prediction during structure extraction.

Purpose of the Study:

  • To introduce a novel method, supervised joint and individual variation explained (sJIVE), for multi-source data analysis.
  • To simultaneously identify shared (joint) and source-specific (individual) underlying data structures.
  • To build a linear prediction model for an outcome using these identified structures.

Main Methods:

  • Developed sJIVE to integrate shared and unique variation from multiple data sources.
  • Weighted components to balance data variation explanation and outcome prediction.
  • Validated through simulations and an application to the COPDGene study.

Main Results:

  • sJIVE effectively identifies both joint and individual structures in multi-source data.
  • The method outperforms existing approaches in simulations with high noise levels.
  • Application to COPDGene data revealed gene expression and proteomic patterns linked to lung function.

Conclusions:

  • sJIVE offers a powerful, integrated approach for multi-source data analysis in biomedical research.
  • The method enhances predictive modeling by considering both shared and unique data variations.
  • sJIVE provides valuable insights into complex biological data, as demonstrated in the COPDGene study.