Correlation is a kind of uncertain interdependence existing in objective phenomena, that is, each value of independent variable. Because the dependent variable is affected by random factors, its corresponding value is uncertain.There is no strict difference between independent variables and dependent variables in correlation analysis, and they can be interchanged.[1]
Chinese name
Correlation
Foreign name
correlation
Also called
Associative relation, class relation, non hierarchical relation
Application
mathematics
Overview
Between categories orSearch termVarious relationships between
⑵ The relationship between the keywords of the concept of unity of opposites;
⑶ The relationship between the keywords of the causal concept;
⑷ The relationship between the retrieval words of the coordinate concept (the ones with closer relationship);
(5) The relationship between the search terms of opposing concepts or contradictory concepts;
(6) The relationship between some antonyms (when both antonyms are used as search words);
(7) The relationship between some synonyms (when both synonyms are used as search words);
(8) The relationship between a certain discipline and the search terms of the research object;
(9) The relationship between a principle or method and the search term of a device or equipment based on it;
(10) The relationship between a certain principle or method and the search term of a certain application;
(11) The relationship between a certain material or equipment and its application search terms;
(12) The relationship between something and its nature or characteristics;
(13) The relationship between a certain discipline, theory, school, group, event and related people, or between an event and the keywords of related groups;
(14) The relationship between a certain behavior and its receptor's search words, etc.[2]
Mathematical variable correlation
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Correlation: When one or several interrelated variables take a certain value, the value of the corresponding other variable is uncertain, but it still follows a certain ruleoneChange within the specified range.This relationship between variables is called uncertainty related relationship.
By degree
⑴Complete correlation: The relationship between two variables. The quantity change of one variable is uniquely determined by the quantity change of the other variable, that is, the functional relationship.
(2) Incomplete correlation: the relationship between the two variables is between the two.
⑶ Uncorrelated: if the quantity changes of two variables are independent of each other, there is no relationship.
By direction
⑴positive correlation: The change trend of the two variables is the same, fromScatter plotIt can be seen that the scattered position of each point is from the lower left corner to the upper right corner, that is, when the value of one variable changes from small to large, the value of the other variable also changes from small to large.
⑵negative correlation: The change trend of the two variables is opposite. It can be seen from the scatter chart that the scattered position of each point is the area from the upper left corner to the lower right corner, that is, when the value of one variable changes from small to large, the value of the other variable changes from large to small.
⑵Complex correlationReflects the correlation between two or more independent variables and the same dependent variable.
⑶partial correlationWhen studying the correlation between the dependent variable and two or more independent variables, if the rest of the independent variables are regarded as invariants (i.e. constants) and only the correlation between the dependent variable and one of the independent variables is studied, it is called partial correlation.[1]
Logical correlation
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Correlation is not equal to causality.causal relationshipIt must be related, but not necessarily causal.
The correlation can exist between them at the same time, and the change of each independent variable may affect the corresponding unique function.Causality only exists between the two, one is cause and the other is effect.
Correlation can provide possibility and be used to speculate causality, but cannot be proved.[1]
Statistical correlation
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The calculation process of correlation coefficient can be expressed as: convert each variable into standard unit, and the average of the product is the correlation coefficient.
The relationship between the two variables can be intuitively represented by a scatter diagram. When they are closely clustered around a straight line, there is a strong correlation between the variables.
A scatter plot can be summarized by five statistics.Average of all x values, SD of all x values, average of all y values, SD of all y values, correlation coefficient r
If the first variable is marked as x, the second variable is marked as y, and the correlation coefficient is r, the following formula can be used:
R=the average of [(x in standard units) X (y in standard units)][2]
