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Related Concept Videos

Correlations02:20

Correlations

36.4K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Correlation and Causation01:27

Correlation and Causation

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
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Correlation01:09

Correlation

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Correlation and Regression00:53

Correlation and Regression

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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Coefficient of Correlation01:12

Coefficient of Correlation

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Correlation Judgment and Visualization Features: A Comparative Study.

Fumeng Yang, Lane T Harrison, Ronald A Rensink

    IEEE Transactions on Visualization and Computer Graphics
    |July 12, 2018
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    Summary
    This summary is machine-generated.

    People perceive correlation in scatterplots by focusing on a few key visual features, not complex statistical models. This finding helps explain previous research and guides future visualization design.

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    Area of Science:

    • Human-Computer Interaction
    • Information Visualization
    • Cognitive Science

    Background:

    • Visualization research increasingly uses vision science principles, like Weber's Law, to model scatterplot correlation perception.
    • Existing models explain perception with simple functions, but the underlying reasons remain unclear.

    Purpose of the Study:

    • Investigate the hypothesis that visual features in charts serve as proxies for statistical measures like correlation.
    • Identify which visual features best correlate with human judgment of scatterplot correlation.

    Main Methods:

    • Extracted 49 candidate visual features from scatterplots.
    • Evaluated the alignment of these features with established perceptual models and participant judgments.

    Main Results:

    • Results support the hypothesis that humans rely on a limited set of visual features to discern correlation.
    • A small subset of visual features effectively models the perception of correlation in scatterplots.

    Conclusions:

    • Human perception of correlation in scatterplots is driven by a few salient visual features.
    • This understanding can reconcile conflicting prior findings and inform the development of future visualization evaluation and design tools.