Pearson correlation coefficient
E665236
The Pearson correlation coefficient is a statistical measure that quantifies the strength and direction of the linear relationship between two continuous variables.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Pearson correlation coefficient canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7454016 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pearson correlation coefficient Context triple: [Karl Pearson, notableWork, Pearson correlation coefficient]
-
A.
Spearman rank-order correlation coefficient
The Spearman rank-order correlation coefficient is a nonparametric statistical measure that assesses the strength and direction of a monotonic relationship between two ranked variables.
-
B.
distance covariance
Distance covariance is a statistical measure that quantifies dependence between random variables, capable of detecting both linear and nonlinear associations.
-
C.
Spearman–Brown prophecy formula
The Spearman–Brown prophecy formula is a psychometric equation used to predict how changes in test length will affect the reliability of a measurement instrument.
-
D.
Fisher's exact test
Fisher's exact test is a statistical significance test used to determine whether there are nonrandom associations between two categorical variables in a contingency table, especially with small sample sizes.
-
E.
Lounsbury correlation
Lounsbury correlation is a proposed scholarly alignment of the Maya Long Count calendar with the Gregorian calendar that offers an alternative to the widely used Goodman–Martínez–Thompson correlation.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Pearson correlation coefficient Target entity description: The Pearson correlation coefficient is a statistical measure that quantifies the strength and direction of the linear relationship between two continuous variables.
-
A.
Spearman rank-order correlation coefficient
The Spearman rank-order correlation coefficient is a nonparametric statistical measure that assesses the strength and direction of a monotonic relationship between two ranked variables.
-
B.
distance covariance
Distance covariance is a statistical measure that quantifies dependence between random variables, capable of detecting both linear and nonlinear associations.
-
C.
Spearman–Brown prophecy formula
The Spearman–Brown prophecy formula is a psychometric equation used to predict how changes in test length will affect the reliability of a measurement instrument.
-
D.
Fisher's exact test
Fisher's exact test is a statistical significance test used to determine whether there are nonrandom associations between two categorical variables in a contingency table, especially with small sample sizes.
-
E.
Lounsbury correlation
Lounsbury correlation is a proposed scholarly alignment of the Maya Long Count calendar with the Gregorian calendar that offers an alternative to the widely used Goodman–Martínez–Thompson correlation.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
correlation coefficient
ⓘ
measure of linear association ⓘ statistical measure ⓘ |
| abbreviation | r ⓘ |
| alsoKnownAs |
Pearson product-moment correlation coefficient
NERFINISHED
ⓘ
product-moment correlation coefficient ⓘ |
| appliesTo | two continuous variables ⓘ |
| assumes |
interval or ratio scale measurement
ⓘ
linear relationship between variables ⓘ |
| canBeTestedBy | t-test for correlation ⓘ |
| category |
descriptive statistics
ⓘ
inferential statistics ⓘ |
| contrastedWith |
Kendall rank correlation coefficient
ⓘ
Spearman rank correlation coefficient ⓘ |
| dependsOn |
covariance of the two variables
ⓘ
standard deviation of each variable ⓘ |
| doesNotMeasure | nonlinear association strength ⓘ |
| field | mathematical statistics ⓘ |
| formula |
r = cov(X,Y) / (σ_X σ_Y)
ⓘ
r = Σ[(x_i - x̄)(y_i - ȳ)] / sqrt[Σ(x_i - x̄)² Σ(y_i - ȳ)²] ⓘ |
| indicates |
negative association when r < 0
ⓘ
positive association when r > 0 ⓘ |
| interpretationAt0 | no linear relationship ⓘ |
| interpretationAt1 | perfect positive linear relationship ⓘ |
| interpretationAtMinus1 | perfect negative linear relationship ⓘ |
| introducedBy | Karl Pearson NERFINISHED ⓘ |
| is | a parametric measure of correlation ⓘ |
| isDimensionless | true ⓘ |
| maximumValue | 1 ⓘ |
| measures |
direction of linear relationship between two variables
ⓘ
strength of linear relationship between two variables ⓘ |
| minimumValue | -1 ⓘ |
| neutralValue | 0 ⓘ |
| oftenAssumesForInference | bivariate normal distribution ⓘ |
| relatedTo |
coefficient of determination
ⓘ
covariance ⓘ simple linear regression ⓘ |
| requires | variables with nonzero variance ⓘ |
| sampleStatisticOf | population correlation coefficient ⓘ |
| sensitiveTo | outliers ⓘ |
| symmetricIn | its two variables ⓘ |
| undefinedWhen | either variable has zero variance ⓘ |
| usedFor |
computing coefficient of determination r² in simple linear regression
ⓘ
measuring effect size ⓘ testing linear association between variables ⓘ |
| usedIn |
data analysis
ⓘ
econometrics ⓘ machine learning feature analysis ⓘ psychometrics ⓘ statistics ⓘ |
| valueRange | -1 to 1 ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Pearson correlation coefficient Description of subject: The Pearson correlation coefficient is a statistical measure that quantifies the strength and direction of the linear relationship between two continuous variables.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.