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Related Concept Videos

Correlations02:20

Correlations

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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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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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Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

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Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
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Related Experiment Video

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Echocardiographic Approaches and Protocols for Comprehensive Phenotypic Characterization of Valvular Heart Disease in Mice
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Comprehensive genotype-phenotype correlation in lissencephaly.

Ai Peng Tan1,2, Wui Khean Chong3, Kshitij Mankad3

  • 1Department of Diagnostic Imaging, National University Health System, Singapore.

Quantitative Imaging in Medicine and Surgery
|September 14, 2018
PubMed
Summary

Malformations of cortical development (MCD) include lissencephaly, a brain disorder caused by defective neuronal migration. Recent advances identified 19 genes linked to lissencephaly, primarily affecting microtubule proteins.

Keywords:
LIS1Lissencephaly (LIS)doublecortin (DCX)tubulinopathy

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Area of Science:

  • Neuroscience
  • Genetics
  • Developmental Biology

Background:

  • Malformations of cortical development (MCD) represent a diverse group of neurological disorders.
  • Lissencephaly, a specific MCD subtype, arises from impaired neuronal migration during fetal development (12-24 weeks gestation).

Purpose of the Study:

  • To review current knowledge on gene mutations implicated in lissencephaly.
  • To establish comprehensive genotype-phenotype correlations for lissencephaly.
  • To use illustrative cases to enhance understanding of these correlations.

Main Methods:

  • Literature review of molecular genetics studies on lissencephaly.
  • Analysis of identified lissencephaly-related genes.
  • Correlation of genetic mutations with observed clinical phenotypes.

Main Results:

  • Over 19 genes associated with lissencephaly have been identified.
  • Most identified genes encode for microtubule structural proteins (tubulin) or microtubule-associated proteins (MAPs).
  • Significant genotypic and phenotypic variations exist within lissencephaly spectrum disorders.

Conclusions:

  • Genetic mutations, particularly in microtubule-related genes, are key causes of lissencephaly.
  • Understanding genotype-phenotype correlations is crucial for diagnosing and managing lissencephaly.
  • Further research into lissencephaly genetics will refine diagnostic and therapeutic strategies.