Synonyms for pearson_correlation_coefficient or Related words with pearson_correlation_coefficient
correlation_coefficient bivariate skewness kullback_leibler_divergence maximum_likelihood_estimator maximum_likelihood_estimate regression_coefficient mahalanobis_distance quantiles chi_squared covariances hypergeometric_distribution informedness probit_model excess_kurtosis quantile covariance_matrices linear_interpolation hellinger_distance covariance_matrix regressor kurtosis confidence_intervals conditional_probabilities univariate weighted_sum probit correlation_coefficients binomial_distribution σx quantile_function regression_coefficients covariance shannon_entropy multivariate_normal conditional_expectation mean_squared_error latent_variables linear_regression unnormalized intraclass_correlation rmsd maximum_likelihood_estimation multinomial latent_variable poisson_distribution youden multinomial_distribution logistic_regression scatterplotExamples of "pearson_correlation_coefficient" |
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where "r" is the Pearson correlation coefficient between the squared deviation scores |
The Spearman correlation coefficient is defined as the Pearson correlation coefficient between the ranked variables. |
The Pearson correlation coefficient is the most commonly used interclass correlation. |
is a multivariate generalization of the "squared" Pearson correlation coefficient (because the RV coefficient takes values between 0 and 1). |
Considering that the Pearson correlation coefficient falls between [−1, 1], the Pearson distance lies in [0, 2]. |
Although computationally the Pearson correlation coefficient reduces to the phi coefficient in the 2×2 case, the interpretation of a Pearson correlation coefficient and phi coefficient must be taken cautiously. The Pearson correlation coefficient ranges from −1 to +1, where ±1 indicates perfect agreement or disagreement, and 0 indicates no relationship. The phi coefficient has a maximum value that is determined by the distribution of the two variables. If both have a 50/50 split, values of phi will range from −1 to +1. See Davenport El-Sanhury (1991) for a thorough discussion. |
Correlation is a measure of relationship between two mathematical variables or measured data values, which includes the Pearson correlation coefficient as a special case. |
Typically formula_7 would be calculated as the Pearson correlation coefficient between the daily log-returns of assets "i" and "j", possibly under zero-mean assumption. |
If the attribute vectors are normalized by subtracting the vector means (e.g., formula_10), the measure is called centered cosine similarity and is equivalent to the Pearson correlation coefficient. |
In an "univariate" linear least squares regression, this is also equals to the squared Pearson correlation coefficient of the dependent formula_12 and explanatory formula_15 variables. |
Pearson's correlation coefficient when applied to a population is commonly represented by the Greek letter "ρ" (rho) and may be referred to as the "population correlation coefficient" or the "population Pearson correlation coefficient". The formula for "ρ" is: |
The coefficient of multiple correlation, denoted "R", is a scalar that is defined as the Pearson correlation coefficient between the predicted and the actual values of the dependent variable in a linear regression model that includes an intercept. |
There's a normalization which derives from first thinking of mutual information as an analogue to covariance (thus Shannon entropy is analogous to variance). Then the normalized mutual information is calculated akin to the Pearson correlation coefficient, |
Classic correspondence analysis is a statistical method that gives a score to every value of two nominal variables. In this way the Pearson correlation coefficient between them is maximized. |
Uncorrelated random variables have a Pearson correlation coefficient of zero, except in the trivial case when either variable has zero variance (is a constant). In this case the correlation is undefined. |
In linear least squares regression with an estimated intercept term, "R" equals the square of the Pearson correlation coefficient between the observed formula_12 and modeled (predicted) formula_13 data values of the dependent variable. |
In statistics, the phi coefficient (also referred to as the "mean square contingency coefficient" and denoted by "φ" (or "r")) is a measure of association for two binary variables. Introduced by Karl Pearson, this measure is similar to the Pearson correlation coefficient in its interpretation. In fact, a Pearson correlation coefficient estimated for two binary variables will return the phi coefficient. The square of the Phi coefficient is related to the chi-squared statistic for a 2×2 contingency table (see Pearson's chi-squared test) |
On the other hand, the Pearson correlation coefficient has been found to provide a link between oxygen uptake and echocardiographic measures. There is also evidence that maximal oxygen consumption and heart size are more important predictors of performance for horses that run longer distances because their energy consumption is mainly aerobic. |
Other important contributions at this time included Charles Spearman's rank correlation coefficient that was a useful extension of the Pearson correlation coefficient. William Sealy Gosset, the English statistician better known under his pseudonym of "Student", introduced Student's t-distribution, a continuous probability distribution useful in situations where the sample size is small and population standard deviation is unknown. |
Both reliability and validity can be assessed statistically. Consistency over repeated measures of the same test can be assessed with the Pearson correlation coefficient, and is often called "test-retest reliability." Similarly, the equivalence of different versions of the same measure can be indexed by a Pearson correlation, and is called "equivalent forms reliability" or a similar term. |